Pour plus d’informations sur ce qu’est un logiciel libre, voir : http://www.gnu.org/philosophy/free-sw.fr.html.
Voir http://rmarkdown.rstudio.com/.
On verra qu’il est possible avec RStudio de disposer d’une telle vue.
Voir par exemple la section dédiée à ce sujet dans le chapitre sur la manipulation de données.

Dans le cas particulier où votre ordinateur est situé derrière un proxy, il est préférable de choisir Options de démarrage personnalisées lorsque cela vous sera demandé par le programme d’installation, puis Internet2 lorsqu’on vous demandera le mode de connexion à Internet. Ainsi, R utilisera par défaut la configuration internet du navigateur Internet Explorer et prendra ainsi en compte les paramètres du proxy.

Fonction	Description
`c`	construit un vecteur à partir d’une série de valeurs
`length`	nombre d’éléments d’un vecteur
`mean`	moyenne d’un vecteur de type numérique
`var`	variance d’un vecteur de type numérique
`+`, `-`, `*`, `/`	opérateurs mathématiques de base
`ˆ`	passage à la puissance

Fonction	Description
`min`	valeur minimale d’un vecteur numérique
`max`	valeur maximale d’un vecteur numérique
`sd`	écart-type d’un vecteur numérique
`:`	génère une séquence de nombres. `1:4` équivaut à `c(1,2,3,4)`

La présence du [1] en début de ligne sera expliquée par la suite dans la section sur les vecteurs.
c est l’abbréviation de combine. Le nom de cette fonction est très court car on l’utilise très souvent.
En bon français, il faudrait dire complètement automatique.

Sous Mac OS X, on utilise les touches Pomme et Entrée.

Quand on veut utiliser des noms de ce type, on doit les entourer avec des backticks (`)
Dans R de base, si une table d contient une colonne qualif, d$qual retournera cette colonne.

Exemple	Classe R	Type
`5L`	integer	nombre entier
`3.14`	numeric	nombre réel
`"abcd"`	character	chaîne de caractères
`TRUE`	logical	booléenne

Classe R	Type
factor	facteur
labelled	vecteur labellisé
Date	date
POSIXct	date et heure

Opérateur de comparaison	Signification
`==`	égal à
`!=`	différent de
`>`	strictement supérieur à
`<`	strictement inférieur à
`>=`	supérieur ou égal à
`<=`	inférieur ou égal à

Opérateur logique	Signification
`&`	et logique
`\|`	ou logique
`!`	négation logique

Dans lequel il sera possible de créer des sous-répertoires.

L’option CSV fonctionne pour tous les fichiers de type texte, même si votre fichier a une autre extension, .txt par exemple

Section	Contenu
Description	donne un résumé en une phrase de ce que fait la fonction
Usage	indique la ou les manières de l’utiliser
Arguments	détaille tous les arguments possibles et leur signification
Value	indique la forme du résultat renvoyé par la fonction
Details	apporte des précisions sur le fonctionnement de la fonction
Note	pour des remarques éventuelles
References	pour des références bibliographiques ou des URL associées
See Also	très utile, renvoie vers d’autres fonctions semblables ou liées, ce qui peut être très utile pour découvrir ou retrouver une fonction dont on a oublié le nom
Examples	série d’exemples d’utilisation

Data summary
Name	hdv2003$cuisine
Number of rows	2000
Number of columns	1
_______________________
Column type frequency:
factor	1
________________________
Group variables	None

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
data	0	1	FALSE	2	Non: 1119, Oui: 881

Data summary
Name	hdv2003
Number of rows	2000
Number of columns	20
_______________________
Column type frequency:
factor	15
numeric	5
________________________
Group variables	None

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
sexe	0	1.00	FALSE	2	Fem: 1101, Hom: 899
nivetud	112	0.94	FALSE	8	Ens: 463, Ens: 441, Der: 341, 1er: 204
occup	0	1.00	FALSE	7	Exe: 1049, Ret: 392, Au : 171, Cho: 134
qualif	347	0.83	FALSE	7	Emp: 594, Ouv: 292, Cad: 260, Ouv: 203
clso	0	1.00	FALSE	3	Non: 1037, Oui: 936, Ne : 27
relig	0	1.00	FALSE	6	App: 760, Pra: 442, Ni : 399, Pra: 266
trav.imp	952	0.52	FALSE	4	Moi: 708, Aus: 259, Peu: 52, Le : 29
trav.satisf	952	0.52	FALSE	3	Sat: 480, Equ: 451, Ins: 117
hard.rock	0	1.00	FALSE	2	Non: 1986, Oui: 14
lecture.bd	0	1.00	FALSE	2	Non: 1953, Oui: 47
peche.chasse	0	1.00	FALSE	2	Non: 1776, Oui: 224
cuisine	0	1.00	FALSE	2	Non: 1119, Oui: 881
bricol	0	1.00	FALSE	2	Non: 1147, Oui: 853
cinema	0	1.00	FALSE	2	Non: 1174, Oui: 826
sport	0	1.00	FALSE	2	Non: 1277, Oui: 723

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100
id	0	1	1000.50	577.49	1.00	500.75	1000.50	1500.25	2000.00
age	0	1	48.16	16.94	18.00	35.00	48.00	60.00	97.00
poids	0	1	5535.61	4375.03	78.08	2221.82	4631.19	7626.54	31092.14
freres.soeurs	0	1	3.28	2.77	0.00	1.00	2.00	5.00	22.00
heures.tv	5	1	2.25	1.78	0.00	1.00	2.00	3.00	12.00

Data summary
Name	hdv2003
Number of rows	2000
Number of columns	20
_______________________
Column type frequency:
factor	2
numeric	2
________________________
Group variables	None

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
relig	0	1	FALSE	6	App: 760, Pra: 442, Ni : 399, Pra: 266
lecture.bd	0	1	FALSE	2	Non: 1953, Oui: 47

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
freres.soeurs	0	1	3.28	2.77	0	1	2	5	22
heures.tv	5	1	2.25	1.78	0	1	2	3	12

Data summary
Name	menages
Number of rows	1814
Number of columns	5
_______________________
Column type frequency:
character	3
numeric	2
________________________
Group variables	None

skim_variable	complete_rate	min	max	n_unique
sexe_chef	1	1	1	2
structure	1	1	1	5
richesse	1	1	1	5

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id_menage	0	1	907.5	523.80	1	454.25	907.5	1360.75	1814
taille	0	1	7.5	4.42	1	4.00	6.0	9.00	31

On priviligiera la fonction to_factor à la fonction as_factor de l’extension haven, la première ayant plus de possibilités et un comportement plus consistent.

Elles sont cependant moins rapides que les fonctions de data.table, voir le chapitre dédié
À noter que cette opération est un peu plus “fragile” que les autres, car si l’ordre des colonnes change elle peut renvoyer un résultat différent.
Il est également possible de renommer des colonnes directement avec select, avec la même syntaxe que pour rename.
Le pipe a été introduit à l’origine par l’extension magrittr, et repris par dplyr

Il s’agit en fait d’une version très simplifiée, la véritable expression permettant de tester si une adresse mail est valide fait plus de 80 lignes…

pivot_longer est une version plus récente et plus robuste de la fonction gather.
pivot_wider est une version plus récente et plus robuste de la fonction spread.

Si vous ne connaissez rien aux langages HTML et CSS, c’est le moment ou jamais d’en apprendre les bases ! Un excellent site de référence pour ce faire est W3 Schools.

feather est un format compatible avec Python, R et Julia. Pour plus d’informations voir https://github.com/wesm/feather

Voir http://fr.wikipedia.org/wiki/Image_matricielle.
Voir http://fr.wikipedia.org/wiki/Image_vectorielle.
Sauf dans le cas des graphiques complexes reposant sur des dégradés de couleurs, comme les cartes produites à partir de rasters. Auquel cas, il sera parfois préférable de privilégier un export dans un format bitmap.
Voir https://www.wikiwand.com/fr/Scalable_Vector_Graphics.
téléchargeable gratuitement sur https://inkscape.org/fr/.

Il existe un grand nombre de couleurs prédéfinies dans R. On peut récupérer leur liste en utilisant la fonction colors en tapant simplement colors() dans la console, ou en consultant le document suivant : http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf.
Voir https://fr.wikipedia.org/wiki/Estimation_par_noyau
Le code ayant servi à générer cette figure est une copie quasi conforme de celui présenté dans l’excellent document de Jean Lobry sur les graphiques de base avec R, téléchargeable sur le site du Pôle bioinformatique lyonnais : http://pbil.univ-lyon1.fr/R/pdf/lang04.pdf.
En l’absence de l’extension questionr, on pourra se rabattre sur la fonction prop.table avec la commande suivante : prop.table(table(d$qualif)).
Voir en particulier https://www.data-to-viz.com/caveat/pie.html pour un exemple concret.
Pour des raisons liées au fonctionnement interne de la fonction dotchart, on doit transformer le tri à plat en matrice, d’où l’appel à la fonction as.matrix.

MASS est installée par défaut avec la version de base de R.
Voir par exemple The boxplot and its pitfalls sur https://www.data-to-viz.com.
La fonction tapply est présentée plus en détails dans le chapitre Manipulation de données.
Il s’agit en fait d’un alias pour les francophones de la fonction rprop.
Sauf s’il est imprimé en noir et blanc…

Voir l’excellente documentation de l’extension et les autres ressources citées en fin de chapitre.
Bien que l’on ait fait le choix de présenter l’extension ggplot2 plutôt que l’extension lattice, celle-ci reste un excellent choix pour la visualisation, notamment, de panels et de séries temporelles. On trouve de très beaux exemples d’utilisation de lattice en ligne, mais un peu moins de documentation, et beaucoup moins d’extensions, que pour ggplot2.
Ce produit est mesuré en termes réels, de manière à ce que le calcul de sa croissance ne soit pas affecté par l’inflation.
Plus précisément, cela introduit un retour à la ligne dans le titre de l’axe.
La régression locale est une variante du calcul de la moyenne glissante (ou moyenne mobile) d’une courbe.

Pour plus de détails, on pourra lire https://tgmstat.wordpress.com/2013/11/13/plot-matrix-with-the-r-package-ggally/.
Pour une présentation détaillée de cette fonction et de ses options, voir https://briatte.github.io/ggcorr/.
Cette extension n’est disponible que sur GitHub. On peut l’installer avec la commande suivante : source("https://install-github.me/larmarange/JLutils")
Pour information, une première implémentation expérimentale avait été proposée avec l’extension productplots, voir https://github.com/hadley/productplots et http://vita.had.co.nz/papers/prodplots.html

On notera que cette variable est utilisée à titre purement illustratif. Le jeu de données étant un extrait d’enquête et la variable de pondération n’ayant pas été recalculée, elle n’a ici à proprement parler aucun sens.
Voir le chapitre régression linéaire.
Voir le chapitre sur la régression logistique.
Voir le chapitre dédié à l’analyse des correspondances.
Pour plus de détails sur les formules, voir le chapitre dédié.
Pour d’autres exemples, voir http://www.ats.ucla.edu/stat/r/faq/svy_r_oscluster.htm (en anglais).
La fonction tapply est présentée plus en détails dans le chapitre Manipulation de données.

Voir le chapitre dédié aux données pondérées.

La fonction tapply est présentée plus en détails dans le chapitre Manipulation de données.
Concernant cette seconde condition, t.test utilise par défaut un test de Welch qui ne suppose pas l’égalité des variances parentes ; il est toutefois possible d’utiliser le test classique de Student en spécifiant l’option var.equal = TRUE.
Ce test peut également fournir un intervalle de confiance avec l’option conf.int=TRUE.
On ne donnera pas plus d’indications sur le test du ² ici. Les personnes désirant une présentation plus détaillée pourront se reporter (attention, séance d’autopromotion !) à la page suivante : http://alea.fr.eu.org/pages/khi2.
On peut aussi appliquer directement le test en spécifiant les deux variables à croiser via chisq.test(d$qualreg, d$sport).
Voir le chapitre dédié aux données pondérées.

Vaste programme d’enquêtes réalisées à intervalles réguliers dans les pays du Sud, disponibles sur http://www.dhsprogram.com/.
L’astuce consiste à utiliser as.integer pour obtenir le code des facteurs et non leur valeur textuelle. L’addition des deux valeurs après multiplication du code de la région par 10 permet d’obtenir une valeur unique pour chaque combinaison des deux variables. On retransforme le résultat en facteurs puis on modifie les étiquettes des modalités.

Il existe également une fonction add.NA fournie avec R. Mais elle ne permet pas de choisir l’étiquette du nouveau niveau créé. Plus spécfiquement, cette étiquette est NA et non une valeur textuelle, ce qui peut créer des problèmes avec certaines fonctions.
Pour plus de détails, voir http://www.spc.univ-lyon1.fr/polycop/odds%20ratio.htm.
Cette extension est livrée avec de nombreuses vignettes dont une vignette d’introduction présentant le fonctionnement des différentes fonctions.
Il existe également des méthodes de sélection ascendante pas à pas, mais nous les aborderons pas ici.
Une alternative est d’avoir recours à l’extension mlogit que nous n’aborderons pas ici. Voir http://www.ats.ucla.edu/stat/r/dae/mlogit.htm (en anglais) pour plus de détails.
Voir le chapitre dédié aux données pondérées.
Compatibilité qui pourra éventuellement être introduite dans une future version de l’extension.
devtools::install_github("larmarange/JLutils") pour l’installer

Pour une présentation plus détaillée, voir
http://www.math.univ-toulouse.fr/~baccini/zpedago/asdm.pdf.
On pourra également avoir recours à la fonction inertia.dudi pour l’ensemble des axes.
La fonction score constituera également une aide à l’interprétation des axes.

Classification ascendante hiérarchique (CAH)

Calculer une matrice des distances
Calcul du dendrogramme
Découper le dendrogramme
CAH avec l’extension FactoMineR

Il existe de nombreuses techniques statistiques visant à partinionner une population en différentes classes ou sous-groupes. La classification ascendante hiérarchique (CAH) est l’une d’entre elles. On cherche à ce que les individus regroupés au sein d’une même classe (homogénéité intra-classe) soient le plus semblables possibles tandis que les classes soient le plus dissemblables (hétérogénéité inter-classe).

Le principe de la CAH est de rassembler des individus selon un critère de ressemblance défini au préalable qui s’exprimera sous la forme d’une matrice de distances, exprimant la distance existant entre chaque individu pris deux à deux. Deux observations identiques auront une distance nulle. Plus les deux observations seront dissemblables, plus la distance sera importante. La CAH va ensuite rassembler les individus de manière itérative afin de produire un dendrogramme ou arbre de classification. La classification est ascendante car elle part des observations individuelles ; elle est hiérarchique car elle produit des classes ou groupes de plus en plus vastes, incluant des sous-groupes en leur sein. En découpant cet arbre à une certaine hauteur choisie, on produira la partition désirée.

On trouvera également de nombreux supports de cours en français sur la CAH sur le site de François Gilles Carpentier : http://pagesperso.univ-brest.fr/~carpenti/.

Calculer une matrice des distances

La notion de ressemblance entre observations est évaluée par une distance entre individus. Plusieurs type de ditances existent selon les données utilisées.

Il existe de nombreuses distances mathématiques pour les variables quantitatives (euclidiennes, Manhattan…) que nous n’aborderons pas ici¹. La plupart peuvent être calculées avec la fonction dist.

Usuellement, pour un ensemble de variables qualitatives, on aura recours à la distance du F² qui est celle utilisée pour l’analyse des correspondances multiples (voir le chapitre dédié). Avec l’extension ade4, la distance du F² s’obtient avec la fonction dist.dudi². Le cas particulier de la CAH avec l’extension FactoMineR sera abordée dans une section spécifique ci-après. Nous évoquerons également la distance de Gower qui peut s’appliquer à un ensemble de variables à la fois qualitatives et quantitatives et qui se calcule avec la fonction daisy de l’extension cluster. Enfin, dans le chapitre sur l’analyse de séquences, nous verrons également la fonction seqdist (extension TraMineR) permettant de calculer une distance entre séquences.

Distance de Gower

En 1971, Gower a proposé un indice de similarité qui porte son nom³. L’objectif de cet indice consiste à mesurer dans quelle mesure deux individus sont semblables. L’indice de Gower varie entre 0 et 1. Si l’indice vaut 1, les deux individus sont identiques. À l’opposé, s’il vaut 0, les deux individus considérés n’ont pas de point commun. Si l’on note S_g l’indice de similarité de Gower, la distance de Gower D_g s’obtient simplement de la manière suivante : D_g = 1 - S_g. Ainsi, la distance sera nulle entre deux individus identiques et elle sera égale à 1 entre deux individus totalement différents. Cette distance s’obtient sous R avec la fonction daisy du package cluster.

L’indice de similarité de Gower entre deux individus x₁ et x₂ se calcule de la manière suivante :

$S_{g} (x_{1}, x_{2}) = \frac{1}{p} j = 1 p s_{12 j} p représente le nombre total de caractères (ou de variables) descriptifs utilisés pour comparer les deux individus 4 . s 12 j représente la similarité partielle entre les individus 1 et 2 concernant le descripteur j . Cette similarité partielle se calcule différemment s’il s’agit d’une variable qualitative ou quantitative : variable qualitative : s 12 j vaut 1 si la variable j prend la même valeur pour les individus 1 et 2, et vaut 0 sinon. Par exemple, si 1 et 2 sont tous les deux « grand », alors s 12 j vaudra 1. Si 1 est « grand » et 2 « petit », s 12 j vaudra 0. variable quantitative : la différence absolue entre les valeurs des deux variables est tout d’abord calculée, soit | y 1 j - y 2 j |. Puis l’écart maximum observé sur l’ensemble du fichier est déterminé et noté R j . Dès lors, la similarité partielle vaut S 12 j = 1 - | y 1 j - y 2 j | / R j . Dans le cas où l’on n’a que des variables qualitatives, la valeur de l’indice de Gower correspond à la proportion de caractères en commun. Supposons des individus 1 et 2 décris ainsi : homme / grand / blond / étudiant / urbain femme / grande / brune / étudiante / rurale Sur les 5 variables utilisées pour les décrire, 1 et 2 ont deux caractéristiques communes : ils sont grand(e)s et étudiant(e)s. Dès lors, l’indice de similarité de Gower entre 1 et 2 vaut 2/5 = 0,4 (soit une distance de 1 - 0,4 = 0,6). Plusieurs approches peuvent être retenues pour traiter les valeurs manquantes : supprimer tout individu n’étant pas renseigné pour toutes les variables de l’analyse ; considérer les valeurs manquantes comme une modalité en tant que telle ; garder les valeurs manquantes en tant que valeurs manquantes. Le choix retenu modifiera les distances de Gower calculées. Supposons que l’on ait : homme / grand / blond / étudiant / urbain femme / grande / brune / étudiante / manquant Si l’on supprime les individus ayant des valeurs manquantes, 2 est retirée du fichier d’observations et aucune distance n’est calculée. Si l’on traite les valeurs manquantes comme une modalité particulière, 1 et 2 partagent alors 2 caractères sur les 5 analysés, la distance de Gower entre eux est alors de 1 - 2/5 =1 - 0,4 = 0,6. Si on garde les valeurs manquantes, l’indice de Gower est dès lors calculé sur les seuls descripteurs renseignés à la fois pour 1 et 2. La distance de Gower sera calculée dans le cas présent uniquement sur les 4 caractères renseignés et vaudra 1 - 2/4 = 0,5. Distance du F² Il s’agit de la distance utilisée dans les analyses de correspondance multiples (ACM). C’est une variante de la distance du ² . Nous considérons ici que nous avons Q questions (soit Q variables initiales de type facteur). À chaque individu est associé un patron c’est-à-dire une certaine combinaison de réponses aux Q questions. La distance entre deux individus correspond à la distance entre leurs deux patrons. Si les deux individus présentent le même patron, leur distance sera nulle. La distance du F² peut s’exprimer ainsi : d_{F^{2}}^{2} (L_{i}, L_{j}) = \frac{1}{Q} k \frac{{(d_{i k} - d_{j k})}^{2}}{f_{k}} où L i et L j sont deux patrons, Q le nombre total de questions. d ik vaut 1 si la modalité k est présente dans le patron L i, 0 sinon. f k est la fréquence de la modalité k dans l’ensemble de la population. Exprimé plus simplement, on fait la somme de l’inverse des modalités non communes aux deux patrons, puis on divise par le nombre total de question. Si nous reprenons notre exemple précédent : homme / grand / blond / étudiant / urbain femme / grande / brune / étudiante / rurale Pour calculer la distance entre 1 et 2, il nous faut connaître la proportion des différentes modalités dans l’ensemble de la population étudiée. En l’occurrence : hommes : 52 % / femmes : 48 % grand : 30 % / moyen : 45 % / petit : 25 % blond : 15 % / châtain : 45 % / brun : 30 % / blanc : 10 % étudiant : 20 % / salariés : 65 % / retraités : 15 % urbain : 80 % / rural : 20 % Les modalités non communes entre les profils de 1 et 2 sont : homme, femme, blond, brun, urbain et rural. La distance du F² entre 1 et 2 est donc la suivante : d_{F^{2}}^{2} (L_{1}, L_{2}) = \frac{1}{5} (\frac{1}{0, 52} + \frac{1}{0, 48} + \frac{1}{0, 15} + \frac{1}{0, 30} + \frac{1}{0, 80} + \frac{1}{0, 20}) = 4, 05 Cette distance, bien que moins intuitive que la distance de Gower évoquée précédemment, est la plus employée pour l’analyse d’enquêtes en sciences sociales. Il faut retenir que la distance entre deux profils est dépendante de la distribution globale de chaque modalité dans la population étudiée. Ainsi, si l’on recalcule les distances entre individus à partir d’un sous-échantillon, le résultat obtenu sera différent. De manière générale, les individus présentant des caractéristiques rares dans la population vont se retrouver éloignés des individus présentant des caractéristiques fortement représentées. Exemple Nous allons reprendre l’ACM calculée avec dudi.acm (ade4) dans le chapitre consacré à l’ACM : library (questionr) data (hdv2003) d <- hdv2003 d $ grpage <- cut (d $ age, c (16, 25, 45, 65, 93), right = FALSE, include.lowest = TRUE) d $ etud <- d $ nivetud levels (d $ etud) <- c ("Primaire", "Primaire", "Primaire", "Secondaire", "Secondaire", "Technique/Professionnel", "Technique/Professionnel", "Supérieur") d2 <- d[, c ("grpage", "sexe", "etud", "peche.chasse", "cinema", "cuisine", "bricol", "sport", "lecture.bd")] library (ade4) acm <- dudi.acm (d2, scannf = FALSE, nf = 5) La matrice des distances s’obtient dès lors avec la fonction dist.dudi : md <- dist.dudi (acm) Calcul du dendrogramme Il faut ensuite choisir une méthode d’ agrégation pour construire le dendrogramme. De nombreuses solutions existent (saut minimum, distance maximum, moyenne, Ward\dots). Chacune d’elle produira un dendrogramme différent. Nous ne détaillerons pas ici ces différentes techniques 5 . Cependant, à l’usage, on privilégiera le plus souvent la méthode de Ward 6 . De manière simplifiée, cette méthode cherche à minimiser l’inertie intra-classe et à maximiser l’inertie inter-classe afin d’obtenir des classes les plus homogènes possibles. Cette méthode est souvent incorrectement présentée comme une méthode de minimisation de la variance alors qu’au sens strict Ward vise l’augmentation mininum de la somme des carrés (“minimum increase of sum-of-squares (of errors)”) 7 . En raison de la variété des distances possibles et de la variété des techniques d’agrégation, on pourra être amené à réaliser plusieurs dendrogrammes différents sur un même jeu de données jusqu’à obtenir une classification qui fait « sens ». La fonction de base pour le calcul d’un dendrogramme est hclust en précisant le critère d’agrégation avec method . Dans notre cas, nous allons opter pour la méthode de Ward appliquée au carré des distances (ce qu’on indique avec method = "ward.D2" 8) : arbre <- hclust (md, method = "ward.D2") Le temps de calcul d’un dendrogramme peut être particulièrement important sur un gros fichier de données. L’extension fastcluster permet de réduire significativement le temps de calcul. Il suffit d’installer puis d’appeler cette extension. La fonction hclust sera automatiquement remplacée par cette version optimisée. Elle prends les mêmes paramètres : library (fastcluster) arbre <- hclust (md, method = "ward.D2") Le dendrogramme obtenu peut être affiché simplement avec plot . Lorsque le nombre d’individus est important, il peut être utile de ne pas afficher les étiquettes des individus avec labels=FALSE . plot (arbre, labels = FALSE, main = "Dendrogramme") Dendrogramme obtenu avec hclust La fonction agnes de l’extension cluster peut également être utilisée pour calculer le dendrogramme. Cependant, à l’usage, elle semble être un peu plus lente que hclust . library (cluster) arbre2 <- agnes (md, method = "ward") ATTENTION : la méthode implémentée dans la fonction agnes correspond à l’option method = "ward.D2" de hclust . Le résultat obtenu n’est pas au même format que celui de hclust . Il est possible de transformer un objet agnes au format hclust avec as.hclust . as.hclust (arbre2) Découper le dendrogramme Pour obtenir une partition de la population, il suffit de découper le dendrogramme obtenu à une certaine hauteur. En premier lieu, une analyse de la forme du dendrogramme pourra nous donner une indication sur le nombre de classes à retenir. Dans notre exemple, deux branches bien distinctes apparaissent sur l’arbre. Pour nous aider, nous pouvons représenter les sauts d’inertie du dendrogramme selon le nombre de classes retenues. inertie <- sort (arbre $ height, decreasing = TRUE) plot (inertie[1 : 20], type = "s", xlab = "Nombre de classes", ylab = "Inertie") Inertie du dendrogramme On voit trois sauts assez nets à 2, 5 et 8 classes, que nous avons représentés ci-dessous respectivement en vert, en rouge et en bleu. plot (inertie[1 : 20], type = "s", xlab = "Nombre de classes", ylab = "Inertie") points (c (2, 5, 8), inertie[c (2, 5, 8)], col = c ("green3", "red3", "blue3"), cex = 2, lwd = 3) Sauts d’inertie du dendrogramme La fonction rect.hclust permet de visualiser les différentes partitions directement sur le dendrogramme. plot (arbre, labels = FALSE, main = "Partition en 2, 5 ou 8 classes", xlab = "", ylab = "", sub = "", axes = FALSE, hang = -1) rect.hclust (arbre, 2, border = "green3") rect.hclust (arbre, 5, border = "red3") rect.hclust (arbre, 8, border = "blue3") Différentes partitions du dendrogramme L’extension FactoMineR (que nous aborderons dans une section dédiée ci-après) suggère d’utiliser la partition ayant la plus grande perte relative d’inertie . L’extension JLutils (disponible sur GitHub) propose une fonction best.cutree qui permet de calculer cette indicateur à partir de n’importe quel dendrogramme calculé avec hclust ou agnes . Pour installer JLutils, on aura recours au package devtools et à sa fonction install_github : library (devtools) install_github ("larmarange/JLutils") Par défaut, best.cutree regarde quelle serait la meilleure partition entre 3 et 20 classes. library (JLutils) best.cutree (arbre) [1] 5 En l’occurence il s’agirait d’une partition en 5 classes. Il est possible de modifier le minimum et le maximum des partitions recherchées avec min et max . best.cutree (arbre, min = 2) [1] 2 On peut également représenter le graphique des pertes relatives d’inertie avec graph=TRUE . La meilleure partition selon ce critère est représentée par un point noir et la seconde par un point gris. best.cutree (arbre, min = 2, graph = TRUE, xlab = "Nombre de classes", ylab = "Inertie relative") [1] 2 Perte relative d’inertie selon le nombre de classes Un découpage en deux classes minimise ce critère. Cependant, si l’on souhaite réaliser une analyse un peu plus fine, un nombre de classes plus élevé serait pertinent. Nous allons donc retenir un découpage en cinq classes. Le découpage s’effectue avec la fonction cutree . typo <- cutree (arbre, 5) freq (typo) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"1031","2":"51.5","3":"51.5","_rn_":"1"},{"1":"164","2":"8.2","3":"8.2","_rn_":"2"},{"1":"553","2":"27.7","3":"27.7","_rn_":"3"},{"1":"205","2":"10.2","3":"10.2","_rn_":"4"},{"1":"47","2":"2.4","3":"2.4","_rn_":"5"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Il existe de multiples autres indicateurs statistiques cherchant à mesurer la qualité de chaque partition. Pour cela, on pourra par exemple avoir recours à la fonction as.clustrange de l’extension WeightedCluster . Pour plus d’informations, voir le manuel de la librairie WeightedCluster, chapitre 7. La typologie obtenue peut être représentée dans le plan factoriel avec s.class . par (mfrow = c (1, 2)) library (RColorBrewer) s.class (acm $ li, as.factor (typo), col = brewer.pal (5, "Set1"), sub = "Axes 1 et 2") s.class (acm $ li, as.factor (typo), 3, 4, col = brewer.pal (5, "Set1"), sub = "Axes 3 et 4") par (mfrow = c (1, 1)) Projection de la typologie obtenue par CAH selon les 4 premiers axes De nombreuses possibilités graphiques sont possibles avec les dendrogrammes. Des exemples documentés sont disponibles à cette adresse : http://rpubs.com/gaston/dendrograms . Romain François a developpé une fonction A2Rplot permettant de réaliser facilement un dendrogramme avec les branches colorées 9 . Par commodité, cette fonction est disponible directement au sein de l’extension JLutils . Pour réaliser le graphique, on indiquera le nombre de classes et les couleurs à utiliser pour chaque branche de l’arbre : A2Rplot (arbre, k = 5, boxes = FALSE, col.up = "gray50", col.down = brewer.pal (5, "Dark2"), show.labels = FALSE) On pourra aussi noter l’extension ggdendro pour représenter des dendrogrammes avec ggplot2 ou encore l’extension dendextend qui permet de manipuler, représenter et comparer des dendrogrammes 10 . CAH avec l’extension FactoMineR L’extension FactoMineR fournit une fonction HCPC permettant de réaliser une classification hiérarchique à partir du résultats d’une analyse factorielle réalisée avec la même extension (voir la section dédiée du chapitre sur l’ACM). HCPC réalise à la fois le calcul de la matrice des distances, du dendrogramme et le partitionnement de la population en classes. Par défaut, HCPC calcule le dendrogramme à partir du carré des distances du F² et avec la méthode de Ward. Par défaut, l’arbre est affiché à l’écran et l’arbre sera coupé selon la partition ayant la plus grande perte relative d’inertie (comme avec best.cutree). Utilisez graph=FALSE pour ne pas afficher le graphique et l’argument nb.clust pour indiquer le nombre de classes désirées. library (FactoMineR) acm2 <- MCA (d2[complete.cases (d2), ], ncp = 5, graph = FALSE) cah <- HCPC (acm2, graph = FALSE) On pourra représenter le dendrogramme avec plot et l’argument choice="tree" . plot (cah, choice = "tree") Dendrogramme obtenu avec HCPC (5 axes) Il apparait que le dendrogramme obtenu avec HCPC diffère de celui que nous avons calculé précédemment en utilisant la matrice des distances fournies par dist.dudi . Cela est dû au fait que HCPC procède différement pour calculer la matrice des distances en ne prenant en compte que les axes retenus dans le cadre de l’ACM. Pour rappel, nous avions retenu que 5 axes dans le cadre de notre ACM : acm2 <- MCA (d2[complete.cases (d2), ], ncp = 5, graph = FALSE) HCPC n’a donc pris en compte que ces 5 premiers axes pour calculer les distances entre les individus, considérant que les autres axes n’apportent que du « bruit » rendant la classification instable. Cependant, comme le montre summary(acm2), nos cinq premiers axes n’expliquent que 54 % de la variance. Il usuellement préférable de garder un plus grande nombre d’axes afin de couvrir au moins 80 à 90 % de la variance 11 . De son côté, dist.dudi prends en compte l’ensemble des axes pour calculer la matrice des distances. On peut reproduire cela avec FactoMineR en indiquant ncp=Inf lors du calcul de l’ACM. acm2 <- MCA (d2[complete.cases (d2), ], ncp = Inf, graph = FALSE) cah <- HCPC (acm2, nb.clust = -1, graph = FALSE) On obtient bien cette fois-ci le même résultat. plot (cah, choice = "tree") Dendrogramme obtenu avec HCPC (tous les axes) D’autres graphiques sont disponibles, en faisant varier la valeur de l’argument choice : plot (cah, choice = "3D.map") Représentation en 3 dimensions du dendrogramme plot (cah, choice = "bar") Gains d’inertie plot (cah, choice = "map") Projection des catégories sur le plan factoriel L’objet renvoyé par HCPC contient de nombreuses informations. La partition peut notamment être récupérée avec cah$data.clust$clust . Il y a également diverses statistiques pour décrire les catégories. cah **Results for the Hierarchical Clustering on Principal Components**
name
1 "$data.clust"
2 "$desc.var"
3 "$desc.var$test.chi2"
4 "$desc.axes$category"
5 "$desc.axes"
6 "$desc.axes$quanti.var"
7 "$desc.axes$quanti"
8 "$desc.ind"
9 "$desc.ind$para"
10 "$desc.ind$dist"
11 "$call"
12 "$call$t"
description
1 "dataset with the cluster of the individuals"
2 "description of the clusters by the variables"
3 "description of the cluster var. by the categorical var."
4 "description of the clusters by the categories."
5 "description of the clusters by the dimensions"
6 "description of the cluster var. by the axes"
7 "description of the clusters by the axes"
8 "description of the clusters by the individuals"
9 "parangons of each clusters"
10 "specific individuals"
11 "summary statistics"
12 "description of the tree" freq (cah $ data.clust $ clust) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"513","2":"27.2","3":"27.2","_rn_":"1"},{"1":"201","2":"10.7","3":"10.7","_rn_":"2"},{"1":"1051","2":"55.7","3":"55.7","_rn_":"3"},{"1":"78","2":"4.1","3":"4.1","_rn_":"4"},{"1":"43","2":"2.3","3":"2.3","_rn_":"5"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Pour une présentation de ces différentes distances, on pourra se référer à http://old.biodiversite.wallonie.be/outils/methodo/similarite_distance.htm ou encore à ce support de cours par D. Chessel, J. Thioulouse et A.B. Dufour disponible à http://pbil.univ-lyon1.fr/R/pdf/stage7.pdf . Cette même fonction peut aussi être utilisée pour calculer une distance après une analyse en composantes principales ou une analyse mixte de Hill et Smith. Voir Gower, J. (1971). A General Coefficient of Similarity and Some of Its Properties. Biometrics, 27(4), 857-871. doi:10.2307/2528823 (http://www.jstor.org/stable/2528823). Pour une description mathématique plus détaillée de cette fonction, notamment en cas de valeur manquante, se référer à l’article original de Gower précédemment cité. On pourra consulter le cours de FG Carpentier déjà cité ou bien des ouvrages d’analyse statistique. Ward, J. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236-244. doi:10.2307/2282967 . (http://www.jstor.org/stable/2282967) Voir par exemple la discussion, en anglais, sur Wikipedia concernant la page présentant la méthode Ward : https://en.wikipedia.org/wiki/Talk:Ward%27s_method Depuis la version 3.1 de R . L’option method = "ward.D" correspondant à la version disponible dans les versions précédentes de R . Mais il est à noter que la méthode décrite par Ward dans son article de 1963 correspond en réalité à method = "ward.D2 . Voir http://addicted2or.free.fr/packages/A2R/lastVersion/R/code.R . Pour une présentation succincte, voir l’introduction de l’extension sur https://cran.r-project.org/web/packages/dendextend/vignettes/introduction.html (en anglais). Voir http://factominer.free.fr/classical-methods/classification-hierarchique-sur-composantes-principales.html Effets d’interaction dans un modèle Exemple d’interaction Un second exemple d’interaction Explorer les différentes interactions possibles Pour aller plus loin Dans un modèle statistique classique, on fait l’hypothèse implicite que chaque variable explicative est indépendante des autres. Cependant, cela ne se vérifie pas toujours. Par exemple, l’effet de l’âge peut varier en fonction du sexe. Il est dès lors nécessaire de prendre en compte dans son modèle les effets d’interaction 1 . Exemple d’interaction Reprenons le modèle que nous avons utilisé dans le chapitre sur la régression logistique . library (questionr) data (hdv2003) d <- hdv2003 d $ sexe <- relevel (d $ sexe, "Femme") d $ grpage <- cut (d $ age, c (16, 25, 45, 65, 99), right = FALSE, include.lowest = TRUE) d $ etud <- d $ nivetud levels (d $ etud) <- c ("Primaire", "Primaire", "Primaire", "Secondaire", "Secondaire", "Technique/Professionnel", "Technique/Professionnel", "Supérieur") d $ etud <- addNAstr (d $ etud, "Manquant") library (labelled) var_label (d $ sport) <- "Pratique du sport ?" var_label (d $ sexe) <- "Sexe" var_label (d $ grpage) <- "Groupe d'âges" var_label (d $ etud) <- "Niveau d'étude" var_label (d $ relig) <- "Pratique religieuse" var_label (d $ heures.tv) <- "Nombre d'heures passées devant la télévision par jour" Nous avions alors exploré les facteurs associés au fait de pratiquer du sport. mod <- glm (sport ~ sexe + grpage + etud + heures.tv + relig, data = d, family = binomial ()) library (gtsummary) tbl_regression (mod, exponentiate = TRUE) html {
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} Characteristic OR 1 95% CI 1 p-value Sexe Femme — — Homme 1.55 1.26, 1.91 <0.001 Groupe d'âges [16,25) — — [25,45) 0.66 0.42, 1.03 0.065 [45,65) 0.34 0.21, 0.54 <0.001 [65,99] 0.25 0.15, 0.43 <0.001 Niveau d'étude Primaire — — Secondaire 2.59 1.77, 3.83 <0.001 Technique/Professionnel 2.86 1.98, 4.17 <0.001 Supérieur 6.63 4.55, 9.80 <0.001 Manquant 8.59 4.53, 16.6 <0.001 Nombre d'heures passées devant la télévision par jour 0.89 0.83, 0.95 <0.001 Pratique religieuse Pratiquant regulier — — Pratiquant occasionnel 0.98 0.68, 1.42 >0.9 Appartenance sans pratique 0.99 0.71, 1.40 >0.9 Ni croyance ni appartenance 0.81 0.55, 1.18 0.3 Rejet 0.68 0.39, 1.19 0.2 NSP ou NVPR 0.92 0.40, 2.02 0.8 1 OR = Odds Ratio, CI = Confidence Interval Selon les résultats de notre modèle, les hommes pratiquent plus un sport que les femmes et la pratique du sport diminue avec l’âge. Pour représenter les effets différentes variables, on peut avoir recours à la fonction allEffects de l’extension effects . library (effects) Loading required package: carData lattice theme set by effectsTheme()
See ?effectsTheme for details. plot (allEffects (mod)) Représentation graphique des effets du modèle Cependant, l’effet de l’âge est-il le même selon le sexe ? Nous allons donc introduire une interaction entre l’âge et le sexe dans notre modèle, ce qui sera représenté par sexe * grpage dans l’équation du modèle. mod2 <- glm (sport ~ sexe * grpage + etud + heures.tv + relig, data = d, family = binomial ()) tbl_regression (mod2, exponentiate = TRUE) html {
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} Characteristic OR 1 95% CI 1 p-value Sexe Femme — — Homme 5.20 2.46, 11.7 <0.001 Groupe d'âges [16,25) — — [25,45) 1.02 0.59, 1.80 >0.9 [45,65) 0.61 0.34, 1.10 0.10 [65,99] 0.46 0.23, 0.92 0.027 Niveau d'étude Primaire — — Secondaire 2.60 1.77, 3.85 <0.001 Technique/Professionnel 2.88 1.99, 4.21 <0.001 Supérieur 6.77 4.64, 10.0 <0.001 Manquant 9.02 4.68, 17.8 <0.001 Nombre d'heures passées devant la télévision par jour 0.89 0.83, 0.95 <0.001 Pratique religieuse Pratiquant regulier — — Pratiquant occasionnel 0.99 0.68, 1.43 >0.9 Appartenance sans pratique 1.02 0.73, 1.44 0.9 Ni croyance ni appartenance 0.83 0.57, 1.22 0.3 Rejet 0.68 0.38, 1.19 0.2 NSP ou NVPR 0.93 0.40, 2.04 0.9 Sexe * Groupe d'âges Homme * [25,45) 0.31 0.13, 0.70 0.006 Homme * [45,65) 0.24 0.10, 0.54 <0.001 Homme * [65,99] 0.23 0.09, 0.59 0.003 1 OR = Odds Ratio, CI = Confidence Interval Commençons par regarder les effets du modèle. plot (allEffects (mod2)) Représentation graphique des effets du modèle avec interaction entre le sexe et le groupe d’âge Sur ce graphique, on voit que l’effet de l’âge sur la pratique d’un sport est surtout marqué chez les hommes. Chez les femmes, le même effet est observé, mais dans une moindre mesure et seulement à partir de 45 ans. On peut tester si l’ajout de l’interaction améliore significativement le modèle avec anova . anova (mod2, test = "Chisq") {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Deviance"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Resid. Df"],"name":[3],"type":["int"],"align":["right"]},{"label":["Resid. Dev"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>Chi)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"NA","2":"NA","3":"1994","4":"2609.203","5":"NA","_rn_":"NULL"},{"1":"1","2":"16.210568","3":"1993","4":"2592.992","5":"5.667706e-05","_rn_":"sexe"},{"1":"3","2":"216.268052","3":"1990","4":"2376.724","5":"1.286530e-46","_rn_":"grpage"},{"1":"4","2":"152.731832","3":"1986","4":"2223.992","5":"5.287542e-32","_rn_":"etud"},{"1":"1","2":"13.587965","3":"1985","4":"2210.404","5":"2.276403e-04","_rn_":"heures.tv"},{"1":"5","2":"4.231856","3":"1980","4":"2206.173","5":"5.165401e-01","_rn_":"relig"},{"1":"3","2":"13.030354","3":"1977","4":"2193.142","5":"4.571421e-03","_rn_":"sexe:grpage"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Jetons maintenant un oeil aux coefficients du modèle. Pour rendre les choses plus visuelles, nous aurons recours à ggcoef de l’extension GGally . library (GGally) ggcoef (mod2, exponentiate = TRUE) Représentation graphique des coefficients du modèle avec interaction entre le sexe et le groupe d’âge Concernant l’âge et le sexe, nous avons trois séries de coefficients : trois coefficients (grpage[25,45), grpage[45,65) et grpage[65,99]) qui correspondent à l’effet global de la variable âge, un coefficient (sexeHomme)pour l’effet global du sexe et trois coefficients qui sont des moficateurs de l’effet d’âge pour les hommes (grpage[25,45), grpage[45,65) et grpage[65,99]). Pour bien interpréter ces coefficients, il faut toujours avoir en tête les modalités choisies comme référence pour chaque variable. Supposons une femme de 60 ans, dont toutes lautres variables correspondent aux modalités de référence (c’est donc une pratiquante régulière, de niveau primaire, qui ne regarde pas la télévision). Regardons ce que prédit le modèle quant à sa probabilité de faire du sport au travers d’une représentation graphique library (breakDown) library (ggplot2) logit <- function (x) exp (x) / (1 + exp (x)) nouvelle_observation <- d[1, ] nouvelle_observation $ sexe[1] = "Femme" nouvelle_observation $ grpage[1] = "[45,65)" nouvelle_observation $ etud[1] = "Primaire" nouvelle_observation $ relig[1] = "Pratiquant regulier" nouvelle_observation $ heures.tv[1] = 0 plot (broken (mod2, nouvelle_observation, predict.function = betas), trans = logit) + ylim (0, 1) + ylab ("Probabilité de faire du sport") Scale for 'y' is already present. Adding another scale
for 'y', which will replace the existing scale. Représentation graphique de l’estimation de la probabilité de faire du sport pour une femme de 60 ans En premier lieu, l’intercept s’applique et permet de déterminer la probabilité de base de faire du sport (si toutes les variables sont à leur valeur de référence). Femme étant la modalité de référence pour la variable sexe, cela ne modifie pas le calcul de la probabilité de faire du sport. Par contre, il y a une modification induite par la modalité 45-65 de la variable grpage . Regardons maintenant la situation d’un homme de 20 ans. nouvelle_observation $ sexe[1] = "Homme" nouvelle_observation $ grpage[1] = "[16,25)" plot (broken (mod2, nouvelle_observation, predict.function = betas), trans = logit) + ylim (0, 1) + ylab ("Probabilité de faire du sport") Scale for 'y' is already present. Adding another scale
for 'y', which will replace the existing scale. Représentation graphique de l’estimation de la probabilité de faire du sport pour un homme de 20 ans Nous sommes à la modalité de référence pour l’âge par contre il y a un effet important du sexe. Le coefficient associé globalement à la variable sexe correspond donc à l’effet du sexe à la modalité de référence du groupe d’âges. La situation est différente pour un homme de 60 ans. nouvelle_observation $ grpage[1] = "[45,65)" plot (broken (mod2, nouvelle_observation, predict.function = betas), trans = logit) + ylim (0, 1) + ylab ("Probabilité de faire du sport") Scale for 'y' is already present. Adding another scale
for 'y', which will replace the existing scale. Représentation graphique de l’estimation de la probabilité de faire du sport pour un homme de 60 ans Cette fois-ci, il y a plusieurs modifications d’effet. On applique en effet à la fois le coefficient sexe = Homme (effet du sexe pour les 15-24 ans), le coefficient grpage = [45-65) qui est l’effet de l’âge pour les femmes de 45-64 ans et le coefficient sexe:grpage = Homme:[45-65) qui indique l’effet spécifique qui s’applique aux hommes de 45-64, d’une part par rapport aux femmes du même et d’autre part par rapport aux hommes de 16-24 ans. L’effet des coefficients d’interaction doivent donc être interprétés par rapport aux autres coefficients du modèle qui s’appliquent, en tenant compte des modalités de référence. Il est cependant possible d’écrire le même modèle différemment. En effet, sexe * grpage dans la formule du modèle est équivalent à l’écriture sexe + grpage + sexe:grpage, c’est-à-dire à modéliser un coefficient global pour chaque variable plus un des coefficients d’interaction. On aurait pu demander juste des coefficients d’interaction, en ne mettant que sexe:grpage . mod3 <- glm (sport ~ sexe : grpage + etud + heures.tv + relig, data = d, family = binomial ()) tbl_regression (mod3, exponentiate = TRUE) html {
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} Characteristic OR 1 95% CI 1 p-value Niveau d'étude Primaire — — Secondaire 2.60 1.77, 3.85 <0.001 Technique/Professionnel 2.88 1.99, 4.21 <0.001 Supérieur 6.77 4.64, 10.0 <0.001 Manquant 9.02 4.68, 17.8 <0.001 Nombre d'heures passées devant la télévision par jour 0.89 0.83, 0.95 <0.001 Pratique religieuse Pratiquant regulier — — Pratiquant occasionnel 0.99 0.68, 1.43 >0.9 Appartenance sans pratique 1.02 0.73, 1.44 0.9 Ni croyance ni appartenance 0.83 0.57, 1.22 0.3 Rejet 0.68 0.38, 1.19 0.2 NSP ou NVPR 0.93 0.40, 2.04 0.9 Sexe * Groupe d'âges Femme * [16,25) 1.82 0.92, 3.58 0.083 Homme * [16,25) 9.46 4.34, 21.8 <0.001 Femme * [25,45) 1.86 1.17, 3.00 0.009 Homme * [25,45) 3.02 1.87, 4.96 <0.001 Femme * [45,65) 1.11 0.69, 1.80 0.7 Homme * [45,65) 1.35 0.84, 2.21 0.2 Femme * [65,99] 0.84 0.47, 1.50 0.6 1 OR = Odds Ratio, CI = Confidence Interval Au sens strict, ce modèle explique tout autant le phénomène étudié que le modèle précédent. On peut le vérifier facilement avec anova . anova (mod2, mod3, test = "Chisq") {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Resid. Df"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["Resid. Dev"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Df"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["Deviance"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>Chi)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"1977","2":"2193.142","3":"NA","4":"NA","5":"NA","_rn_":"1"},{"1":"1977","2":"2193.142","3":"0","4":"0","5":"NA","_rn_":"2"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} De même, les effets modélisés sont les mêmes. plot (allEffects (mod3)) Représentation graphique des effets du modèle avec interaction simple entre le sexe et le groupe d’âge Par contre, regardons d’un peu plus près les coefficients de ce nouveau modèle. Nous allons voir que leur interprétation est légèrement différente. ggcoef (mod3, exponentiate = TRUE) Représentation graphique des coefficients du modèle avec interaction simple entre le sexe et le groupe d’âge Cette fois-ci, il n’y a plus de coefficients globaux pour la variable sexe ni pour grpage mais des coefficients pour chaque combinaison de ces deux variables. plot (broken (mod3, nouvelle_observation, predict.function = betas), trans = logit) + ylim (0, 1) + ylab ("Probabilité de faire du sport") Scale for 'y' is already present. Adding another scale
for 'y', which will replace the existing scale. Représentation graphique de l’estimation de la probabilité de faire du sport pour un homme de 40 ans Cette fois-ci, le coefficient d’interaction fourrnit l’effet global du sexe et de l’âge, et non plus la modification de cette combinaison par rapport aux coefficients globaux. Leur sens est donc différent et il faudra les interpréter en conséquence. Un second exemple d’interaction Intéressons-nous maintenant à l’interaction entre le sexe et le niveau d’étude. L’effet du niveau d’étude diffère-t-il selon le sexe ? mod4 <- glm (sport ~ sexe * etud + grpage + heures.tv + relig, data = d, family = binomial ()) Regardons d’abord les effets. plot (allEffects (mod4)) Représentation graphique des effets du modèle avec interaction entre le sexe et le niveau d’étude À première vue, l’effet du niveau d’étude semble être le même chez les hommes et chez les femmes. Ceci dit, cela serait peut être plus lisible si l’on superposait les deux sexe sur un même graphique. Nous allons utiliser la fonction ggeffect de l’extension ggeffects qui permets de récupérer les effets calculés avec effect dans un format utilisable avec ggplot2 . library (ggeffects) Attaching package: 'ggeffects' The following object is masked from 'package:cowplot':

get_title plot (ggeffect (mod4, c ("etud", "sexe"))) Effets du niveau d’étude selon le sexe Cela confirme ce que l’on suppose. Regardons les coefficients du modèle. ggcoef (mod4, exponentiate = TRUE) Représentation graphique des coefficients du modèle avec interaction simple entre le sexe et le niveau d’étude tbl_regression (mod4, exponentiate = TRUE) html {
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} Characteristic OR 1 95% CI 1 p-value Sexe Femme — — Homme 1.42 0.77, 2.58 0.3 Niveau d'étude Primaire — — Secondaire 2.25 1.35, 3.82 0.002 Technique/Professionnel 3.09 1.90, 5.17 <0.001 Supérieur 6.59 4.02, 11.1 <0.001 Manquant 5.21 2.42, 11.4 <0.001 Groupe d'âges [16,25) — — [25,45) 0.66 0.42, 1.04 0.071 [45,65) 0.34 0.21, 0.55 <0.001 [65,99] 0.25 0.15, 0.43 <0.001 Nombre d'heures passées devant la télévision par jour 0.89 0.83, 0.95 <0.001 Pratique religieuse Pratiquant regulier — — Pratiquant occasionnel 0.98 0.67, 1.42 0.9 Appartenance sans pratique 1.01 0.72, 1.43 >0.9 Ni croyance ni appartenance 0.83 0.56, 1.21 0.3 Rejet 0.70 0.40, 1.23 0.2 NSP ou NVPR 0.90 0.39, 1.98 0.8 Sexe * Niveau d'étude Homme * Secondaire 1.38 0.65, 2.93 0.4 Homme * Technique/Professionnel 0.87 0.44, 1.75 0.7 Homme * Supérieur 1.01 0.49, 2.07 >0.9 Homme * Manquant 4.28 1.32, 15.8 0.020 1 OR = Odds Ratio, CI = Confidence Interval Si les coefficients associés au niveau d’étude sont significatifs, ceux de l’interaction ne le sont pas (sauf sexeHomme:etudManquant) et celui associé au sexe, précédemment significatif ne l’est plus. Testons avec anova si l’interaction est belle et bien significative. anova (mod4, test = "Chisq") {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Df"],"name":[1],"type":["int"],"align":["right"]},{"label":["Deviance"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Resid. Df"],"name":[3],"type":["int"],"align":["right"]},{"label":["Resid. Dev"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Pr(>Chi)"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"NA","2":"NA","3":"1994","4":"2609.203","5":"NA","_rn_":"NULL"},{"1":"1","2":"16.210568","3":"1993","4":"2592.992","5":"5.667706e-05","_rn_":"sexe"},{"1":"4","2":"319.190017","3":"1989","4":"2273.802","5":"7.843331e-68","_rn_":"etud"},{"1":"3","2":"49.809867","3":"1986","4":"2223.992","5":"8.769868e-11","_rn_":"grpage"},{"1":"1","2":"13.587965","3":"1985","4":"2210.404","5":"2.276403e-04","_rn_":"heures.tv"},{"1":"5","2":"4.231856","3":"1980","4":"2206.173","5":"5.165401e-01","_rn_":"relig"},{"1":"4","2":"10.222660","3":"1976","4":"2195.950","5":"3.683946e-02","_rn_":"sexe:etud"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} L’interaction est bien significative mais faiblement. Vu que l’effet du niveau d’étude reste nénamoins très similaire selon le sexe, on peut se demander s’il est pertinent de la conserver. Explorer les différentes interactions possibles Il peut y avoir de multiples interactions dans un modèle, d’ordre 2 (entre deux variables) ou plus (entre trois variables ou plus). Il est dès lors tentant de tester les multiples interactions possibles de manière itératives afin d’identifier celles à retenir. C’est justement le but de la fonction glmulti de l’extension du même nom. glmulti permets de tester toutes les combinaisons d’interactions d’ordre 2 dans un modèle, en retenant le meilleur modèle à partir d’un critère spécifié (par défaut l’ AIC). ATTENTION : le temps de calcul de glmulti peut-être long. library (glmulti) glmulti (sport ~ sexe + grpage + etud + heures.tv + relig, data = d, family = binomial ()) Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: sport~1+grpage+heures.tv+sexe:heures.tv+grpage:heures.tv+etud:heures.tv
Crit= 2284.87861987263
Mean crit= 2406.80086471225

After 100 models:
Best model: sport~1+etud+heures.tv+grpage:heures.tv
Crit= 2267.79462883348
Mean crit= 2360.46497457747

After 150 models:
Best model: sport~1+grpage+etud+heures.tv+sexe:heures.tv
Crit= 2228.88574082404
Mean crit= 2286.60589884071

After 200 models:
Best model: sport~1+grpage+etud+heures.tv+sexe:heures.tv
Crit= 2228.88574082404
Mean crit= 2254.99359340075

After 250 models:
Best model: sport~1+sexe+grpage+etud+heures.tv+etud:sexe+sexe:heures.tv
Crit= 2226.00088609349
Mean crit= 2241.76611580481

After 300 models:
Best model: sport~1+sexe+grpage+etud+heures.tv+grpage:sexe+sexe:heures.tv
Crit= 2222.67161519005
Mean crit= 2234.95020358944 On voit qu’au bout d’un moment, l’algorithme se statibilise autour d’un modèle comportant une interaction entre le sexe et l’âge d’une part et entre le sexe et le nombre d’heures passées quotidiennement devant la télé. On voit également que la variable religion a été retirée du modèle final. best <- glm (sport ~ 1 + sexe + grpage + etud + heures.tv + grpage : sexe + sexe : heures.tv, data = d, family = binomial ()) odds.ratio (best) Waiting for profiling to be done... {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["OR"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["2.5 %"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["97.5 %"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["p"],"name":[4],"type":["dbl"],"align":["right"]}],"data":[{"1":"0.2853948","2":"0.14481105","3":"0.5557226","4":"2.507671e-04","_rn_":"(Intercept)"},{"1":"4.0187321","2":"1.80857301","3":"9.4145359","4":"8.948753e-04","_rn_":"sexeHomme"},{"1":"1.0302051","2":"0.59131212","3":"1.8183799","4":"9.170428e-01","_rn_":"grpage[25,45)"},{"1":"0.6275988","2":"0.35186815","3":"1.1323269","4":"1.171763e-01","_rn_":"grpage[45,65)"},{"1":"0.4934766","2":"0.24910600","3":"0.9761597","4":"4.212028e-02","_rn_":"grpage[65,99]"},{"1":"2.5419890","2":"1.73557252","3":"3.7634119","4":"2.210614e-06","_rn_":"etudSecondaire"},{"1":"2.8175695","2":"1.95625089","3":"4.1136841","4":"4.442864e-08","_rn_":"etudTechnique/Professionnel"},{"1":"6.6850848","2":"4.58853819","3":"9.8795998","4":"2.358862e-22","_rn_":"etudSupérieur"},{"1":"8.8175667","2":"4.57501584","3":"17.3667592","4":"1.446616e-10","_rn_":"etudManquant"},{"1":"0.8457505","2":"0.77151872","3":"0.9237122","4":"2.611928e-04","_rn_":"heures.tv"},{"1":"0.3180038","2":"0.13443374","3":"0.7155436","4":"6.962634e-03","_rn_":"sexeHomme:grpage[25,45)"},{"1":"0.2425282","2":"0.10136186","3":"0.5524224","4":"1.003409e-03","_rn_":"sexeHomme:grpage[45,65)"},{"1":"0.2246351","2":"0.08375307","3":"0.5805492","4":"2.419033e-03","_rn_":"sexeHomme:grpage[65,99]"},{"1":"1.1153509","2":"0.97939734","3":"1.2704828","4":"9.972658e-02","_rn_":"sexeHomme:heures.tv"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} ggcoef (best, exponentiate = TRUE) Représentation graphique des coefficients du modèle avec interaction entre le sexe, le niveau d’étude et le nombre d’heures passées devant la télévision plot (allEffects (best)) Représentation graphique des effets du modèle avec interaction entre le sexe, le niveau d’étude et le nombre d’heures passées devant la télévision Pour aller plus loin Il y a d’autres extensions dédiées à l’analyse des interactions d’un modèle, de même que de nombreux supports de cours en ligne dédiés à cette question. On pourra en particulier se référer à la vignette inclue avec l’extension phia : https://cran.r-project.org/web/packages/phia/vignettes/phia.pdf . Pour une présentation plus statistique et mathématique des effets d’interaction, on pourra se référer au cours de Jean-François Bickel disponible en ligne . Multicolinéarité dans la régression Définition Mesure de la colinéarité La multicolinéarité est-elle toujours un problème ? Dans une régression, la multicolinéarité est un problème qui survient lorsque certaines variables de prévision du modèle mesurent le même phénomène. Une multicolinéarité prononcée s’avère problématique, car elle peut augmenter la variance des coefficients de régression et les rendre instables et difficiles à interpréter. Les conséquences de coefficients instables peuvent être les suivantes : les coefficients peuvent sembler non significatifs, même lorsqu’une relation significative existe entre le prédicteur et la réponse ; les coefficients de prédicteurs fortement corrélés varieront considérablement d’un échantillon à un autre ; lorsque des termes d’un modèle sont fortement corrélés, la suppression de l’un de ces termes aura une incidence considérable sur les coefficients estimés des autres. Les coefficients des termes fortement corrélés peuvent même présenter le mauvais signe. La multicolinéarité n’a aucune incidence sur l’adéquation de l’ajustement, ni sur la qualité de la prévision. Cependant, les coefficients individuels associés à chaque variable explicative ne peuvent pas être interprétés de façon fiable. Définition Au sens strict, on parle de multicolinéarité parfaite lorsqu’une des variables explicatives d’un modèle est une combinaison linéraire d’une ou plusieurs autres variables explicatives introduites dans le même modèle. L’absence de multicolinéarité parfaite est une des conditions requises pour pouvoir estimer un modèle linéaire et, par extension, un modèle linéaire généralisé (dont les modèles de régression logistique). Dans les faits, une multicolinéarité parfaite n’est quasiment jamais observée. Mais une forte multicolinéarité entre plusieurs variables peut poser problème dans l’estimation et l’interprétation d’un modèle. Une erreur fréquente est de confondre multicolinéarité et corrélation . Si des variables colinéaires sont de facto fortement corrélées entre elles, deux variables corrélées ne sont pas forcément colinéaires. En termes non statistiques, il y a colinéarité lorsque deux ou plusieurs variables mesurent la même chose . Prenons un exemple. Nous étudions les complications après l’accouchement dans différentes maternités d’un pays en développement. On souhaite mettre dans le modèle, à la fois le milieu de résidence (urbain ou rural) et le fait qu’il y ait ou non un médecin dans la clinique. Or, dans la zone d’enquête, les maternités rurales sont dirigées seulement par des sage-femmes tandis que l’on trouve un médecin dans toutes les maternités urbaines sauf une. Dès lors, dans ce contexte précis, le milieu de résidence prédit presque totalement la présence d’un médecin et on se retrouve face à une multicolinéarité (qui serait même parfaite s’il n’y avait pas une clinique urbaine sans médecin). On ne peut donc distinguer l’effet de la présence d’un médecin de celui du milieu de résidence et il ne faut mettre qu’une seule de ces deux variables dans le modèle, sachant que du point de vue de l’interprétation elle capturera à la fois l’effet de la présence d’un médecin et celui du milieu de résidence. Par contre, si dans notre région d’étude, seule la moitié des maternités urbaines disposait d’un médecin, alors le milieu de résidence n’aurait pas été suffisant pour prédire la présence d’un médecin. Certes, les deux variables seraient corrélées mais pas colinéaires. Un autre exemple de corrélation sans colinéarité, c’est la relation entre milieu de résidence et niveau d’instruction. Il y a une corrélation entre ces deux variables, les personnes résidant en ville étant généralement plus instruites. Cependant, il existe également des personnes non instruites en ville et des personnes instruites en milieu rural. Le milieu de résidence n’est donc pas suffisant pour prédire le niveau d’instruction. Mesure de la colinéarité Il existe différentes mesures de la multicolinéarité. L’extension mctest en fournie plusieurs, mais elle n’est utilisable que si l’ensemble des variables explicatives sont de type numérique. L’approche la plus classique consiste à examiner les facteurs d’inflation de la variance (FIV) ou variance inflation factor (VIF) en anglais. Les FIV estimenent de combien la variance d’un coefficient est augmentée en raison d’une relation linéaire avec d’autres prédicteurs. Ainsi, un FIV de 1,8 nous dit que la variance de ce coefficient particulier est supérieure de 80 % à la variance que l’on aurait dû observer si ce facteur n’est absolument pas corrélé aux autres prédicteurs. Si tous les FIV sont égaux à 1, il n’existe pas de multicolinéarité, mais si certains FIV sont supérieurs à 1, les prédicteurs sont corrélés. Il n’y a pas de consensus sur la valeur au-delà de laquelle on doit considérer qu’il y a multicolinéarité. Certains auteurs, comme Paul Allison 1, disent regarder plus en détail les variables avec un FIV supérieur à 2,5. D’autres ne s’inquiètent qu’à partir de 5. Il n’existe pas de test statistique qui permettrait de dire s’il y a colinéarité ou non 2 . L’extension car fournit une fonction vif permettant de calculer les FIV à partir d’un modèle. Elle implémente même une version généralisée permettant de considérer des facteurs catégoriels et des modèles linéaires généralisés comme la régression logistique. Reprenons, pour exemple, un modèle logistique que nous avons déjà abordé dans d’autres chapitres. library (questionr) data (hdv2003) d <- hdv2003 d $ sexe <- relevel (d $ sexe, "Femme") d $ grpage <- cut (d $ age, c (16, 25, 45, 65, 99), right = FALSE, include.lowest = TRUE) d $ etud <- d $ nivetud levels (d $ etud) <- c ("Primaire", "Primaire", "Primaire", "Secondaire", "Secondaire", "Technique/Professionnel", "Technique/Professionnel", "Supérieur") d $ etud <- addNAstr (d $ etud, "Manquant") mod <- glm (sport ~ sexe + grpage + etud + heures.tv + relig, data = d, family = binomial ()) Le calcul des FIV se fait simplement en passant le modèle à la fonction vif . library (car) Loading required package: carData Registered S3 methods overwritten by 'car':
method from
influence.merMod lme4
cooks.distance.influence.merMod lme4
dfbeta.influence.merMod lme4
dfbetas.influence.merMod lme4 Attaching package: 'car' The following object is masked _by_ '.GlobalEnv':

logit The following object is masked from 'package:dplyr':

recode vif (mod) GVIF Df GVIF^(1/(2*Df))
sexe 1.048 1 1.024
grpage 1.842 3 1.107
etud 1.848 4 1.080
heures.tv 1.062 1 1.031
relig 1.121 5 1.011 Dans notre exemple, tous les FIV sont proches de 1. Il n’y a donc pas de problème potentiel de colinéarité à explorer. La multicolinéarité est-elle toujours un problème ? Là encore, il n’y a pas de consensus sur cette question. Certains analystes considèrent que tout modèle où certains prédicteurs seraient colinéaires n’est pas valable. Dans un billet sur internet, Paul Allison évoque quant à lui des situations où la multicolinéarité peut être ignorée en toute sécurité. Le texte ci-dessous est une traduction du billet de Paul Allison. 1. Les variables avec des FIV élevés sont des variables de contrôle, et les variables d’intérêt n’ont pas de FIV élevés. Voici le problème de la multicollinéarité : ce n’est un problème que pour les variables qui sont colinéaires. Il augmente les erreurs-types de leurs coefficients et peut rendre ces coefficients instables de plusieurs façons. Mais tant que les variables colinéaires ne sont utilisées que comme variables de contrôle, et qu’elles ne sont pas colinéaires avec vos variables d’intérêt, il n’y a pas de problème. Les coefficients des variables d’intérêt ne sont pas affectés et la performance des variables de contrôle n’est pas altérée. Voici un exemple tiré de ces propres travaux : l’échantillon est constitué de collèges américains, la variable dépendante est le taux d’obtention de diplôme et la variable d’intérêt est un indicateur (factice) pour les secteurs public et privé. Deux variables de contrôle sont les scores moyens au SAT et les scores moyens à l’ACT pour l’entrée en première année. Ces deux variables ont une corrélation supérieure à ,9, ce qui correspond à des FIV d’au moins 5,26 pour chacune d’entre elles. Mais le FIV pour l’indicateur public/privé n’est que de 1,04. Il n’y a donc pas de problème à se préoccuper et il n’est pas nécessaire de supprimer l’un ou l’autre des deux contrôles, à condition que l’on ne cherche pas à interpréter ou comparer l’un par rapport à l’autre les coefficients de ces deux variables de contrôle. 2. Les FIV élevés sont causés par l’inclusion de puissances ou de produits d’autres variables. Si vous spécifiez un modèle de régression avec x et x 2, il y a de bonnes chances que ces deux variables soient fortement corrélées. De même, si votre modèle a x, z et xz, x et z sont susceptibles d’être fortement corrélés avec leur produit. Il n’y a pas de quoi s’inquiéter, car la valeur p de xz n’est pas affectée par la multicollinéarité. Ceci est facile à démontrer : vous pouvez réduire considérablement les corrélations en centrant les variables (c’est-à-dire en soustrayant leurs moyennes) avant de créer les puissances ou les produits. Mais la valeur p pour x 2 ou pour xz sera exactement la même, que l’on centre ou non. Et tous les résultats pour les autres variables (y compris le R 2 mais sans les termes d’ordre inférieur) seront les mêmes dans les deux cas. La multicollinéarité n’a donc pas de conséquences négatives. 3. Les variables avec des FIV élevés sont des variables indicatrices (factices) qui représentent une variable catégorielle avec trois catégories ou plus. Si la proportion de cas dans la catégorie de référence est faible, les variables indicatrices auront nécessairement des FIV élevés, même si la variable catégorielle n’est pas associée à d’autres variables dans le modèle de régression. Supposons, par exemple, qu’une variable de l’état matrimonial comporte trois catégories : actuellement marié, jamais marié et anciennement marié. Vous choisissez anciennement marié comme catégorie de référence, avec des variables d’indicateur pour les deux autres. Ce qui se passe, c’est que la corrélation entre ces deux indicateurs devient plus négative à mesure que la fraction de personnes dans la catégorie de référence diminue. Par exemple, si 45 % des personnes ne sont jamais mariées, 45 % sont mariées et 10 % sont anciennement mariées, les valeurs du FIV pour les personnes mariées et les personnes jamais mariées seront d’au moins 3,0. Est-ce un problème ? Eh bien, cela signifie que les valeurs p des variables indicatrices peuvent être élevées. Mais le test global selon lequel tous les indicateurs ont des coefficients de zéro n’est pas affecté par des FIV élevés. Et rien d’autre dans la régression n’est affecté. Si vous voulez vraiment éviter des FIV élevés, il suffit de choisir une catégorie de référence avec une plus grande fraction des cas. Cela peut être souhaitable pour éviter les situations où aucun des indicateurs individuels n’est statistiquement significatif, même si l’ensemble des indicateurs est significatif. https://statisticalhorizons.com/multicollinearity Pour plus de détails, voir ce post de Davig Giles qui explique pourquoi ce n’est pas possible. Analyse de survie Ressources en ligne Un exemple concret : mortalité infanto-juvénile Préparation des données avec data.table Préparation des données avec dplyr Kaplan-Meier Modèle de Cox Vérification de la validité du modèle Données pondérées Ressources en ligne L’extension centrale pour l’ analyse de survie est survival . Un très bon tutoriel (en anglais et en 3 étapes), introduisant les concepts de l’analyse de survie, des courbes de Kaplan-Meier et des modèles de Cox et leur mise en oeuvre pratique sous R est disponible en ligne : http://www.sthda.com/english/wiki/survival-analysis-basics http://www.sthda.com/english/wiki/cox-proportional-hazards-model http://www.sthda.com/english/wiki/cox-model-assumptions Pour un autre exemple (toujours en anglais) d’analyse de survie avec survival, on pourra se référer à https://rpubs.com/vinubalan/hrsurvival . Pour représenter vos résultats avec ggplot2, on pourra avoir recours à l’extension survminer présentée en détails sur son site officiel (en anglais) : http://www.sthda.com/english/rpkgs/survminer/ . On pourra également avoir recours à la fonction ggsurv de l’extension GGally présentée à l’adresse http://ggobi.github.io/ggally/#ggallyggsurv . A noter, il est possible d’utiliser la fonction step sur un modèle de Cox, pour une sélection pas à pas d’un meilleur modèle basé sur une minimisation de l’AIC (voir le chapitre sur la régression logistique). L’excellente extension broom peut également être utilisée sur des modèles de survie (Kaplan-Meier ou Cox) pour en convertir les résultats sous la forme d’un tableau de données. Pour approfondir les possibilités offertes par l’extension survival, on pourra également consulter les différentes vignettes fournies avec l’extension (voir https://cran.r-project.org/package=survival). Un exemple concret : mortalité infanto-juvénile Dans cet exemple, nous allons utiliser le jeu de données fecondite fourni par l’extension questionr . Ce jeu de données comporte trois tableaux de données : menages, femmes et enfants . Nous souhaitons étudier ici la survie des enfants entre la naissance et l’âge de 5 ans. Dans un premier temps, nous comparerons la survie des jeunes filles et des jeunes garçons. Dans un second temps, nous procéderons à une analyse multivariée en prenant en compte les variables suivantes : sexe de l’enfant milieu de résidence niveau de vie du ménage structure du ménage niveau d’éducation de la mère âge de la mère à la naissance de l’enfant enfin, une variable un peu plus compliquée, à savoir si le rang de naissance de l’enfant (second, troisième, quatrième, etc.) est supérieur au nombre idéal d’enfants selon la mère. Nous allons préparer les données selon deux approches : soit en utilisant l’extension data.table (voir le chapitre dédié à data.table), soit en utilisant l’extension dplyr (voir le chapitre sur dplyr). Chargeons les données en mémoire et listons les variables disponibles. library (questionr, quietly = TRUE) data (fecondite) lookfor (menages) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["variable"],"name":[1],"type":["chr"],"align":["left"]},{"label":["label"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"id_menage","2":"Identifiant du ménage","_rn_":"1"},{"1":"taille","2":"Taille du ménage (nombre de membres)","_rn_":"2"},{"1":"sexe_chef","2":"Sexe du chef de ménage","_rn_":"3"},{"1":"structure","2":"Structure démographique du ménage","_rn_":"4"},{"1":"richesse","2":"Niveau de vie (quintiles)","_rn_":"5"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} lookfor (femmes) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["variable"],"name":[1],"type":["chr"],"align":["left"]},{"label":["label"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"id_femme","2":"Identifiant de l'enquêtée","_rn_":"1"},{"1":"id_menage","2":"Identifiant du ménage","_rn_":"2"},{"1":"poids","2":"Poids statistique","_rn_":"3"},{"1":"date_entretien","2":"Date de passation du questionnaire","_rn_":"4"},{"1":"date_naissance","2":"Date de naissance","_rn_":"5"},{"1":"age","2":"Âge révolu (en années) à la date de passation du questionnaire","_rn_":"6"},{"1":"milieu","2":"Milieu de résidence","_rn_":"7"},{"1":"region","2":"Région de résidence","_rn_":"8"},{"1":"educ","2":"Niveau d'éducation","_rn_":"9"},{"1":"travail","2":"A un emploi ?","_rn_":"10"},{"1":"matri","2":"Statut matrimonial","_rn_":"11"},{"1":"religion","2":"Religion","_rn_":"12"},{"1":"journal","2":"Lit la presse ?","_rn_":"13"},{"1":"radio","2":"Ecoute la radio ?","_rn_":"14"},{"1":"tv","2":"Regarde la télévision ?","_rn_":"15"},{"1":"nb_enf_ideal","2":"Nombre idéal d'enfants","_rn_":"16"},{"1":"test","2":"A déjà fait un test de dépistage du VIH ?","_rn_":"17"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} lookfor (enfants) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["variable"],"name":[1],"type":["chr"],"align":["left"]},{"label":["label"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"id_enfant","2":"Identifiant de l'enfant","_rn_":"1"},{"1":"id_femme","2":"Identifiant de la mère","_rn_":"2"},{"1":"date_naissance","2":"Date de naissance","_rn_":"3"},{"1":"sexe","2":"Sexe de l'enfant","_rn_":"4"},{"1":"survie","2":"L'enfant est-il toujours en vie ?","_rn_":"5"},{"1":"age_deces","2":"Age au décès (en mois)","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Préparation des données avec data.table Tout d’abord, regardons sous quel format elles sont stockées. class (menages) [1] "tbl_df" "tbl" "data.frame" describe (menages) [1814 obs. x 5 variables] tbl_df tbl data.frame

$id_menage: Identifiant du ménage
numeric: 1 2 3 4 5 6 7 8 9 10 ...
min: 1 - max: 1814 - NAs: 0 (0%) - 1814 unique values

$taille: Taille du ménage (nombre de membres)
numeric: 7 3 6 5 7 6 15 6 5 19 ...
min: 1 - max: 31 - NAs: 0 (0%) - 30 unique values

$sexe_chef: Sexe du chef de ménage
labelled double: 2 1 1 1 1 2 2 2 1 1 ...
min: 1 - max: 2 - NAs: 0 (0%) - 2 unique values
2 value labels: [1] homme [2] femme

$structure: Structure démographique du ménage
labelled double: 4 2 5 4 4 4 5 2 5 5 ...
min: 1 - max: 5 - NAs: 0 (0%) - 5 unique values
6 value labels: [0] pas d'adulte [1] un adulte [2] deux adultes de sexe opposé [3] deux adultes de même sexe [4] trois adultes ou plus avec lien de parenté [5] adultes sans lien de parenté

$richesse: Niveau de vie (quintiles)
labelled double: 1 2 2 1 1 3 2 5 4 3 ...
min: 1 - max: 5 - NAs: 0 (0%) - 5 unique values
5 value labels: [1] très pauvre [2] pauvre [3] moyen [4] riche [5] très riche Les tableaux de données sont au format tibble (c’est-à-dire sont de la classe tbl_df) et les variables catégorielles sont du type haven_labelled (voir le chapitre sur les vecteurs labellisés). Ce format correspond au format de données si on les avait importées depuis SPSS avec l’extension haven (voir le chapitre sur l’ import de données). En premier lieu, il nous faut convertir les tableaux de données au format data.table, ce qui peut se faire avec la fonction setDT . Par ailleurs, nous allons également charger en mémoire l’extension labelled pour la gestion des vecteurs labellisés. library (labelled) library (data.table) setDT (menages) setDT (femmes) setDT (enfants) En premier lieu, il nous faut calculer la durée d’observation des enfants, à savoir le temps passé entre la date de naissance (variable du fichier enfants) et la date de passation de l’entretien (fournie par le tableau de données femmes). Pour récupérer des variables du fichier femmes dans le fichier enfants, nous allons procéder à une fusion de table (voir le chapitre dédié). Pour le calcul de la durée d’observation, nous allons utiliser le package lubridate (voir le chapitre calculer un âge et celui sur la gestion des dates). Nous effectuerons l’analyse en mois (puisque l’âge au décès est connu en mois). Dès lors, la durée d’observation sera calculée en mois. enfants <- merge (enfants, femmes[, .(id_femme, date_entretien)], by = "id_femme", all.x = TRUE) # duree observation en mois library (lubridate, quietly = TRUE) enfants[, duree_observation : = time_length (interval (date_naissance, date_entretien), unit = "months")] ATTENTION : il y 11 enfants soi-disant nés après la date d’enquête ! Quelle que soit l’enquête, il est rare de ne pas observer d’incohérences. Dans le cas présent, il est fort possible que la date d’entretien puisse parfois être erronnée (par exemple si l’enquêteur a inscrit une date sur le questionnaire papier le jour du recensement du ménage mais n’ai pu effectué le questionnaire individuel que plus tard). Nous décidons ici de procéder à une correction en ajoutant un mois aux dates d’entretien problématiques. D’autres approches auraient pu être envisagées, comme par exemple exclure ces observations problématiques. Cependant, cela aurait impacté le calcul du range de naissance pour les autres enfants issus de la même mère. Quoiqu’il en soit, il n’y a pas de réponse unique. À vous de vous adapter au contexte particulier de votre analyse. enfants[duree_observation < 0, date_entretien : = date_entretien %m+% months (1)] enfants[, duree_observation : = time_length (interval (date_naissance, date_entretien), unit = "months")] Regardons maintenant comment les âges au décès ont été collectés. freq (enfants $ age_deces) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"62","2":"3.9","3":"43.7","_rn_":"0"},{"1":"5","2":"0.3","3":"3.5","_rn_":"1"},{"1":"6","2":"0.4","3":"4.2","_rn_":"2"},{"1":"5","2":"0.3","3":"3.5","_rn_":"3"},{"1":"4","2":"0.3","3":"2.8","_rn_":"4"},{"1":"5","2":"0.3","3":"3.5","_rn_":"5"},{"1":"5","2":"0.3","3":"3.5","_rn_":"6"},{"1":"4","2":"0.3","3":"2.8","_rn_":"7"},{"1":"3","2":"0.2","3":"2.1","_rn_":"8"},{"1":"6","2":"0.4","3":"4.2","_rn_":"9"},{"1":"1","2":"0.1","3":"0.7","_rn_":"10"},{"1":"5","2":"0.3","3":"3.5","_rn_":"11"},{"1":"10","2":"0.6","3":"7.0","_rn_":"12"},{"1":"3","2":"0.2","3":"2.1","_rn_":"13"},{"1":"1","2":"0.1","3":"0.7","_rn_":"14"},{"1":"1","2":"0.1","3":"0.7","_rn_":"16"},{"1":"2","2":"0.1","3":"1.4","_rn_":"19"},{"1":"1","2":"0.1","3":"0.7","_rn_":"21"},{"1":"10","2":"0.6","3":"7.0","_rn_":"24"},{"1":"2","2":"0.1","3":"1.4","_rn_":"36"},{"1":"1","2":"0.1","3":"0.7","_rn_":"48"},{"1":"1442","2":"91.0","3":"NA","_rn_":"NaN"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Les âges au décès sont ici exprimés en mois révolus. Les décès à un mois révolu correspondent à des décès entre 1 et 2 mois exacts. Par ailleurs, les durées d’observation que nous avons calculées avec time_length sont des durées exactes, c’est-à-dire avec la partie décimale. Pour une analyse de survie, on ne peut mélanger des durées exactes et des durées révolues. Trois approches peuvent être envisagées : faire l’analyse en mois révolus, auquel cas on ne gardera que la partie entière des durées d’observations avec la fonction trunc; considérer qu’un âge au décès de 3 mois révolus correspond en moyenne à 3,5 mois exacts et donc ajouter 0,5 à tous les âges révolus ; imputer un âge au décès exact en distribuant aléatoirement les décès à 3 mois révolus entre 3 et 4 mois exacts, autrement dit en ajoutant aléatoirement une partie décimale aux âges révolus. Nous allons ici adopter la troisième approche en considérant que les décès se répartissent de manière uniforme au sein d’un même mois. Nous aurons donc recours à la fonction runif qui permets de générer des valeurs aléatoires entre 0 et 1 selon une distribustion uniforme. enfants[, age_deces_impute : = age_deces + runif (.N)] Pour définir notre objet de survie, il nous faudra deux variables. Une première, temporelle, indiquant la durée à laquelle survient l’évènement étudié (ici le décès) pour ceux ayant vécu l’évènement et la durée d’observation pour ceux n’ayant pas vécu l’évènement (censure à droite). Par ailleurs, une seconde variable indiquant si les individus ont vécu l’évènement (0 pour non, 1 pour oui). Or, ici, la variable survie est codée 0 pour les décès et 1 pour ceux ayant survécu. Pour plus de détails, voir l’aide de la fonction Surv . enfants[, deces : = 0] enfants[survie == 0, deces : = 1] var_label (enfants $ deces) <- "Est décédé ?" val_labels (enfants $ deces) <- c (non = 0, oui = 1) enfants[, time : = duree_observation] enfants[deces == 1, time : = age_deces_impute] Occupons-nous maintenant des variables explicatives que nous allons inclure dans l’analyse. Tout d’abord, ajoutons à la table enfants les variables nécessaires des tables femmes et menages . Notons qu’il nous faudra importer id_menage de la table femmes pour pouvoir fusionner ensuite la table enfants avec la table menages . Par ailleurs, pour éviter une confusion sur la variable date_naissance, nous renommons à la volée cette variable de la table femmes en date_naissance_mere . enfants <- merge (enfants, femmes[, .(id_femme, id_menage, milieu, educ, date_naissance_mere = date_naissance, nb_enf_ideal)], by = "id_femme", all.x = TRUE) enfants <- merge (enfants, menages[, .(id_menage, structure, richesse)], by = "id_menage", all.x = TRUE) Les variables catégorielles sont pour l’heure sous formes de vecteurs labellisés. Or, dans un modèle, il est impératif de les convertir en facteurs pour qu’elles soient bien traitées comme des variables catégorielles (autrement elles seraient traitées comme des variables continues). On aura donc recours à la fonction to_factor de l’extension labelled . enfants[, sexe : = to_factor (sexe)] enfants[, richesse : = to_factor (richesse)] Regardons plus attentivement, la variable structure . freq (enfants $ structure) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"0","2":"0.0","3":"0.0","_rn_":"[0] pas d'adulte"},{"1":"45","2":"2.8","3":"2.8","_rn_":"[1] un adulte"},{"1":"505","2":"31.9","3":"31.9","_rn_":"[2] deux adultes de sexe opposé"},{"1":"71","2":"4.5","3":"4.5","_rn_":"[3] deux adultes de même sexe"},{"1":"764","2":"48.2","3":"48.2","_rn_":"[4] trois adultes ou plus avec lien de parenté"},{"1":"199","2":"12.6","3":"12.6","_rn_":"[5] adultes sans lien de parenté"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Tout d’abord, la modalité pas d’adulte n’est pas représentée dans l’échantillon. On aura donc recours à l’argument drop_unused_labels pour ne pas conserver cette modalité. Par ailleurs, nous considérons que la situation familiale à partir de laquelle nous voudrons comparer les autres dans notre modèle, donc celle qui doit être considérée comme la modalité de référence, est celle du ménage nucléaire. Cette modalité (deux adultes de sexe opposé) n’étant pas la première, nous aurons recours à la fonction relevel . enfants[, structure : = to_factor (structure, drop_unused_labels = TRUE)] enfants[, structure : = relevel (structure, "deux adultes de sexe opposé")] Regardons la variable educ . freq (enfants $ educ) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"1084","2":"68.4","3":"68.4","_rn_":"[0] aucun"},{"1":"368","2":"23.2","3":"23.2","_rn_":"[1] primaire"},{"1":"115","2":"7.3","3":"7.3","_rn_":"[2] secondaire"},{"1":"17","2":"1.1","3":"1.1","_rn_":"[3] supérieur"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} La modalité supérieur est peu représentée dans notre échantillon. Nous allons la fusionner avec la modalité secondaire (voir la section Regrouper les modalités d’une variable du chapitre Recodage). enfants[, educ2 : = educ] enfants[educ == 3, educ2 : = 2] val_label (enfants $ educ2, 2) <- "secondaire ou plus" val_label (enfants $ educ2, 3) <- NULL enfants[, educ2 : = to_factor (educ2)] freq (enfants $ educ2) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"1084","2":"68.4","3":"68.4","_rn_":"aucun"},{"1":"368","2":"23.2","3":"23.2","_rn_":"primaire"},{"1":"132","2":"8.3","3":"8.3","_rn_":"secondaire ou plus"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Calculons maintenant l’âge de la mère à la naissance de l’enfant (voir le chapitre Calculer un âge) et découpons le en groupes d’âges (voir la section Découper une variable numérique en classes du chapitre Recodage). enfants[, age_mere_naissance : = time_length (interval (date_naissance_mere, date_naissance), unit = "years")] enfants $ gpage_mere_naissance <- cut (enfants $ age_mere_naissance, include.lowest = TRUE, right = FALSE, breaks= c (13, 20, 30, 50)) levels (enfants $ gpage_mere_naissance) <- c ("19 ou moins", "20-29", "30 et plus") enfants $ gpage_mere_naissance <- relevel (enfants $ gpage_mere_naissance, "20-29") freq (enfants $ gpage_mere_naissance) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"888","2":"56.1","3":"56.1","_rn_":"20-29"},{"1":"257","2":"16.2","3":"16.2","_rn_":"19 ou moins"},{"1":"439","2":"27.7","3":"27.7","_rn_":"30 et plus"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Reste à calculer si le rang de naissance de l’enfant est supérieur au nombre idéal d’enfants tel que défini par la mère. On aura recours à la fonction rank appliquée par groupe (ici calculé séparément pour chaque mère). L’argument ties.method permet d’indiquer comment gérer les égalités (ici les naissances multiples, e.g. les jumeaux). Comme nous voulons comparer le rang de l’enfant au nombre idéal d’enfants, nous allons retenir la méthode "max" pour obtenir, dans le cas présent, le nombre total d’enfants déjà nés 1 . Avant de calculer un rang, il est impératif de trier préalablement le tableau (voir le chapitre Tris). setorder (enfants, id_femme, date_naissance) enfants[, rang : = rank (date_naissance, ties.method = "max"), by = id_femme] enfants[, rang_apres_ideal : = "non"] enfants[rang > nb_enf_ideal, rang_apres_ideal : = "oui"] enfants[, rang_apres_ideal : = factor (rang_apres_ideal)] enfants[, rang_apres_ideal : = relevel (rang_apres_ideal, "non")] Préparation des données avec dplyr Tout d’abord, regardons sous quel format elles sont stockées. data (fecondite) class (menages) [1] "tbl_df" "tbl" "data.frame" describe (menages) [1814 obs. x 5 variables] tbl_df tbl data.frame

$id_menage: Identifiant du ménage
numeric: 1 2 3 4 5 6 7 8 9 10 ...
min: 1 - max: 1814 - NAs: 0 (0%) - 1814 unique values

$taille: Taille du ménage (nombre de membres)
numeric: 7 3 6 5 7 6 15 6 5 19 ...
min: 1 - max: 31 - NAs: 0 (0%) - 30 unique values

$sexe_chef: Sexe du chef de ménage
labelled double: 2 1 1 1 1 2 2 2 1 1 ...
min: 1 - max: 2 - NAs: 0 (0%) - 2 unique values
2 value labels: [1] homme [2] femme

$structure: Structure démographique du ménage
labelled double: 4 2 5 4 4 4 5 2 5 5 ...
min: 1 - max: 5 - NAs: 0 (0%) - 5 unique values
6 value labels: [0] pas d'adulte [1] un adulte [2] deux adultes de sexe opposé [3] deux adultes de même sexe [4] trois adultes ou plus avec lien de parenté [5] adultes sans lien de parenté

$richesse: Niveau de vie (quintiles)
labelled double: 1 2 2 1 1 3 2 5 4 3 ...
min: 1 - max: 5 - NAs: 0 (0%) - 5 unique values
5 value labels: [1] très pauvre [2] pauvre [3] moyen [4] riche [5] très riche Les tableaux de données sont déjà au format tibble (c’est-à-dire sont de la classe tbl_df) 2 et les variables catégorielles sont du type labelled (voir le chapitre sur les vecteurs labellisés). Ce format correspond au format de données si on les avait importées depuis SPSS avec l’extension haven (voir le chapitre sur l’ import de données). Nous allons charger en mémoire l’extension labelled pour la gestion des vecteurs labellisés en plus de dplyr . library (dplyr) library (labelled) En premier lieu, il nous faut calculer la durée d’observation des enfants, à savoir le temps passé entre la date de naissance (variable du fichier enfants) et la date de passation de l’entretien (fournie par le tableau de données femmes). Pour récupérer des variables du fichier femmes dans le fichier enfants, nous allons procéder à une fusion de table (voir le chapitre dédié). Pour le calcul de la durée d’observation, nous allons utiliser le package lubridate (voir le chapitre calculer un âge et celui sur la gestion des dates). Nous effectuerons l’analyse en mois (puisque l’âge au décès est connu en mois). Dès lors, la durée d’observation sera calculée en mois. ATTENTION : les étiquettes de valeurs sont le plus souvent perdus lors des fusions de tables avec dplyr . Pour les récupérer après fusion, nous allons conserver une version originale du tableau de données enfants et utiliser la fonction copy_labels_from de l’extension labelled . enfants_original <- enfants library (lubridate) enfants <- enfants %>% left_join (femmes %>% select (id_femme, date_entretien), by = "id_femme") %>% copy_labels_from (enfants_original) %>% mutate (duree_observation = time_length (interval (date_naissance, date_entretien), unit = "months")) Warning: Column `id_femme` has different attributes on LHS
and RHS of join ATTENTION : il y 11 enfants soi-disant nés après la date d’enquête ! Quelle que soit l’enquête, il est rare de ne pas observer d’incohérences. Dans le cas présent, il est fort possible que la date d’entretien puisse parfois être erronnée (par exemple si l’enquêteur a inscrit une date sur le questionnaire papier le jour du recensement du ménage mais n’ai pu effectué le questionnaire individuel que plus tard). Nous décidons ici de procéder à une correction en ajoutant un mois aux dates d’entretien problématiques. D’autres approches auraient pu être envisagées, comme par exemple exclure ces observations problématiques. Cependant, cela aurait impacté le calcul du range de naissance pour les autres enfants issus de la même mère. Quoiqu’il en soit, il n’y a pas de réponse unique. À vous de vous adapter au contexte particulier de votre analyse. enfants $ date_entretien[enfants $ duree_observation < 0] <- enfants $ date_entretien[enfants $ duree_observation < 0] %m+% months (1) enfants <- enfants %>% mutate (duree_observation = time_length (interval (date_naissance, date_entretien), unit = "months")) Regardons maintenant comment les âges au décès ont été collectés. freq (enfants $ age_deces) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"62","2":"3.9","3":"43.7","_rn_":"0"},{"1":"5","2":"0.3","3":"3.5","_rn_":"1"},{"1":"6","2":"0.4","3":"4.2","_rn_":"2"},{"1":"5","2":"0.3","3":"3.5","_rn_":"3"},{"1":"4","2":"0.3","3":"2.8","_rn_":"4"},{"1":"5","2":"0.3","3":"3.5","_rn_":"5"},{"1":"5","2":"0.3","3":"3.5","_rn_":"6"},{"1":"4","2":"0.3","3":"2.8","_rn_":"7"},{"1":"3","2":"0.2","3":"2.1","_rn_":"8"},{"1":"6","2":"0.4","3":"4.2","_rn_":"9"},{"1":"1","2":"0.1","3":"0.7","_rn_":"10"},{"1":"5","2":"0.3","3":"3.5","_rn_":"11"},{"1":"10","2":"0.6","3":"7.0","_rn_":"12"},{"1":"3","2":"0.2","3":"2.1","_rn_":"13"},{"1":"1","2":"0.1","3":"0.7","_rn_":"14"},{"1":"1","2":"0.1","3":"0.7","_rn_":"16"},{"1":"2","2":"0.1","3":"1.4","_rn_":"19"},{"1":"1","2":"0.1","3":"0.7","_rn_":"21"},{"1":"10","2":"0.6","3":"7.0","_rn_":"24"},{"1":"2","2":"0.1","3":"1.4","_rn_":"36"},{"1":"1","2":"0.1","3":"0.7","_rn_":"48"},{"1":"1442","2":"91.0","3":"NA","_rn_":"NaN"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Les âges au décès sont ici exprimés en mois révolus. Les décès à un mois révolu correspondent à des décès entre 1 et 2 mois exacts. Par ailleurs, les durées d’observation que nous avons calculées avec time_length sont des durées exactes, c’est-à-dire avec la partie décimale. Pour une analyse de survie, on ne peut mélanger des durées exactes et des durées révolues. Trois approches peuvent être envisagées : faire l’analyse en mois révolus, auquel cas on ne gardera que la partie entière des durées d’observations avec la fonction trunc; considérer qu’un âge au décès de 3 mois révolus correspond en moyenne à 3,5 mois exacts et donc ajouter 0,5 à tous les âges révolus ; imputer un âge au décès exact en distribuant aléatoirement les décès à 3 mois révolus entre 3 et 4 mois exacts, autrement dit en ajoutant aléatoirement une partie décimale aux âges révolus. Nous allons ici adopter la troisième approche en considérant que les décès se répartissent de manière uniforme au sein d’un même mois. Nous aurons donc recours à la fonction runif qui permets de générer des valeurs aléatoires entre 0 et 1 selon une distribustion uniforme. enfants <- enfants %>% dplyr :: mutate (age_deces_impute = age_deces + runif (n ())) Pour définir notre objet de survie, il nous faudra deux variables. Une première, temporelle, indiquant la durée à laquelle survient l’évènement étudié (ici le décès) pour ceux ayant vécu l’évènement et la durée d’observation pour ceux n’ayant pas vécu l’évènement (censure à droite). Par ailleurs, une seconde variable indiquant si les individus ont vécu l’évènement (0 pour non, 1 pour oui). Or, ici, la variable survie est codée 0 pour les décès et 1 pour ceux ayant survécu. Pour plus de détails, voir l’aide de la fonction Surv . enfants <- enfants %>% mutate (deces = if_else (survie == 0, 1, 0)) %>% set_variable_labels (deces = "Est décédé ?") %>% set_value_labels (deces = c (non = 0, oui = 1)) %>% mutate (time = if_else (deces == 1, age_deces_impute, duree_observation)) Occupons-nous maintenant des variables explicatives que nous allons inclure dans l’analyse. Tout d’abord, ajoutons à la table enfants les variables nécessaires des tables femmes et menages . Notons qu’il nous faudra importer id_menage de la table femmes pour pouvoir fusionner ensuite la table enfants avec la table menages . Par ailleurs, pour éviter une confusion sur la variable date_naissance, nous renommons à la volée cette variable de la table femmes en date_naissance_mere . enfants <- enfants %>% left_join (select (femmes, id_femme, id_menage, milieu, educ, date_naissance_mere = date_naissance, nb_enf_ideal), by = "id_femme") %>% left_join (select (menages, id_menage, structure, richesse), by = "id_menage") %>% copy_labels_from (enfants_original) %>% copy_labels_from (femmes) %>% copy_labels_from (menages) Warning: Column `id_femme` has different attributes on LHS
and RHS of join Les variables catégorielles sont pour l’heure sous formes de vecteurs labellisés. Or, dans un modèle, il est impératif de les convertir en facteurs pour qu’elles soient bien traitées comme des variables catégorielles (autrement elles seraient traitées comme des variables continues). On aura donc recours à la fonction to_factor de l’extension labelled . enfants <- enfants %>% mutate (sexe = to_factor (sexe), richesse = to_factor (richesse)) Regardons plus attentivement, la variable structure . freq (enfants $ structure) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"0","2":"0.0","3":"0.0","_rn_":"[0] pas d'adulte"},{"1":"45","2":"2.8","3":"2.8","_rn_":"[1] un adulte"},{"1":"505","2":"31.9","3":"31.9","_rn_":"[2] deux adultes de sexe opposé"},{"1":"71","2":"4.5","3":"4.5","_rn_":"[3] deux adultes de même sexe"},{"1":"764","2":"48.2","3":"48.2","_rn_":"[4] trois adultes ou plus avec lien de parenté"},{"1":"199","2":"12.6","3":"12.6","_rn_":"[5] adultes sans lien de parenté"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Tout d’abord, la modalité pas d’adulte n’est pas représentée dans l’échantillon. On aura donc recours à l’argument drop_unused_labels pour ne pas conserver cette modalité. Par ailleurs, nous considérons que la situation familiale à partir de laquelle nous voudrons comparer les autres dans notre modèle, donc celle qui doit être considérée comme la modalité de référence, est celle du ménage nucléaire. Cette modalité (deux adultes de sexe opposé) n’étant pas la première, nous aurons recours à la fonction relevel {data-pkg = “stats”}. enfants <- enfants %>% mutate (structure = relevel (to_factor (structure, drop_unused_labels = TRUE), "deux adultes de sexe opposé")) Regardons la variable educ . freq (enfants $ educ) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"1084","2":"68.4","3":"68.4","_rn_":"[0] aucun"},{"1":"368","2":"23.2","3":"23.2","_rn_":"[1] primaire"},{"1":"115","2":"7.3","3":"7.3","_rn_":"[2] secondaire"},{"1":"17","2":"1.1","3":"1.1","_rn_":"[3] supérieur"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} La modalité supérieur est peu représentée dans notre échantillon. Nous allons la fusionner avec la modalité secondaire (voir la section Regrouper les modalités d’une variable du chapitre Recodage). enfants <- enfants %>% mutate (educ2 = ifelse (educ == 3, 2, educ)) %>% set_value_labels (educ2 = c (aucun = 0, primaire = 1, "secondaire ou plus" = 2)) %>% mutate (educ2 = to_factor (educ2)) freq (enfants $ educ2) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"1084","2":"68.4","3":"68.4","_rn_":"aucun"},{"1":"368","2":"23.2","3":"23.2","_rn_":"primaire"},{"1":"132","2":"8.3","3":"8.3","_rn_":"secondaire ou plus"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Calculons maintenant l’âge de la mère à la naissance de l’enfant (voir le chapitre Caluler un âge) et découpons le en groupes d’âges (voir la section Découper une variable numérique en classes du chapitre Recodage). enfants <- enfants %>% mutate (age_mere_naissance = time_length (interval (date_naissance_mere, date_naissance), unit = "years"), gpage_mere_naissance = cut (age_mere_naissance, include.lowest = TRUE, right = FALSE, breaks= c (13, 20, 30, 50))) levels (enfants $ gpage_mere_naissance) <- c ("19 ou moins", "20-29", "30 et plus") enfants $ gpage_mere_naissance <- relevel (enfants $ gpage_mere_naissance, "20-29") freq (enfants $ gpage_mere_naissance) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"888","2":"56.1","3":"56.1","_rn_":"20-29"},{"1":"257","2":"16.2","3":"16.2","_rn_":"19 ou moins"},{"1":"439","2":"27.7","3":"27.7","_rn_":"30 et plus"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Reste à calculer si le rang de naissance de l’enfant est supérieur au nombre idéal d’enfants tel que défini par la mère. On aura recours à la fonction rank appliquée par groupe (ici calculé séparément pour chaque mère). L’argument ties.method permet d’indiquer comment gérer les égalités (ici les naissances multiples, e.g. les jumeaux). Comme nous voulons comparer le rang de l’enfant au nombre idéal d’enfants, nous allons retenir la méthode "max" pour obtenir, dans le cas présent, le nombre total d’enfants déjà nés 3 . Avant de calculer un rang, il est impératif de trier préalablement le tableau (voir le chapitre Tris). enfants <- enfants %>% arrange (id_femme, date_naissance) %>% group_by (id_femme) %>% mutate (rang = rank (date_naissance, ties.method = "max"), rang_apres_ideal = ifelse (rang > as.integer (nb_enf_ideal), "oui", "non"), rang_apres_ideal = factor (rang_apres_ideal, levels = c ("non", "oui"))) Kaplan-Meier La courbe de survie de Kaplan-Meier s’obtient avec la fonction survfit de l’extension survival . library (survival) km_global <- survfit (Surv (time, deces) ~ 1, data = enfants) km_global Call: survfit(formula = Surv(time, deces) ~ 1, data = enfants)

n events median 0.95LCL 0.95UCL
1584 142 NA NA NA Pour la représenter, on pourra avoir recours à la fonction ggsurvplot de l’extension survminer . library (survminer, quietly = TRUE) ggsurvplot (km_global) Courbe de survie de Kaplan-Meier On peut facilement représenter à la place la courbe cumulée des évènements (l’inverse de la courbe de survie) et la table des effectifs en fonction du temps. ggsurvplot (km_global, fun = "event", risk.table = TRUE, surv.scale = "percent") Courbe cumulée des évènements et table des effectifs Pour comparer deux groupes (ici les filles et les garçons), il suffit d’indiquer la variable de comparaison à survfit . km_sexe <- survfit (Surv (time, deces) ~ sexe, data = enfants) km_sexe Call: survfit(formula = Surv(time, deces) ~ sexe, data = enfants)

n events median 0.95LCL 0.95UCL
sexe=masculin 762 94 NA NA NA
sexe=féminin 822 48 NA NA NA La fonction survdiff permets de calculer le test du logrank afin de comparer des courbes de survie. La mortalité infanto-juvénile diffère-t-elle significativement selon le sexe de l’enfant ? survdiff (Surv (time, deces) ~ sexe, data = enfants) Call:
survdiff(formula = Surv(time, deces) ~ sexe, data = enfants)

N Observed Expected (O-E)^2/E (O-E)^2/V
sexe=masculin 762 94 66.2 11.6 21.8
sexe=féminin 822 48 75.8 10.2 21.8

Chisq= 21.8 on 1 degrees of freedom, p= 3e-06 Une fois encore, on aura recours à ggsurvplot pour représenter les courbes de survie. ggsurvplot (km_sexe, conf.int = TRUE, risk.table = TRUE, pval = TRUE, data = enfants) Warning: Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2. Courbes de Kaplan-Meier selon le sexe Modèle de Cox Un modèle de Cox se calcule aisément avec coxph {survival}. mod1 <- coxph (Surv (time, deces) ~ sexe + milieu + richesse + structure + educ2 + gpage_mere_naissance + rang_apres_ideal, data = enfants) mod1 Call:
coxph(formula = Surv(time, deces) ~ sexe + milieu + richesse +
structure + educ2 + gpage_mere_naissance + rang_apres_ideal,
data = enfants)

coef
sexeféminin -0.809568
milieu 0.656241
richessepauvre -0.082223
richessemoyen 0.318645
richesseriche 0.353483
richessetrès riche 0.464590
structureun adulte -0.150231
structuredeux adultes de même sexe 0.604592
structuretrois adultes ou plus avec lien de parenté 0.049430
structureadultes sans lien de parenté -0.131369
educ2primaire -0.030251
educ2secondaire ou plus -0.203903
gpage_mere_naissance19 ou moins -0.310248
gpage_mere_naissance30 et plus -0.002586
rang_apres_idealoui 1.355106
exp(coef)
sexeféminin 0.445050
milieu 1.927533
richessepauvre 0.921066
richessemoyen 1.375263
richesseriche 1.424019
richessetrès riche 1.591361
structureun adulte 0.860509
structuredeux adultes de même sexe 1.830506
structuretrois adultes ou plus avec lien de parenté 1.050672
structureadultes sans lien de parenté 0.876895
educ2primaire 0.970202
educ2secondaire ou plus 0.815541
gpage_mere_naissance19 ou moins 0.733265
gpage_mere_naissance30 et plus 0.997417
rang_apres_idealoui 3.877173
se(coef)
sexeféminin 0.177806
milieu 0.269928
richessepauvre 0.250417
richessemoyen 0.247868
richesseriche 0.298425
richessetrès riche 0.428583
structureun adulte 0.600329
structuredeux adultes de même sexe 0.376445
structuretrois adultes ou plus avec lien de parenté 0.196666
structureadultes sans lien de parenté 0.305478
educ2primaire 0.205751
educ2secondaire ou plus 0.366889
gpage_mere_naissance19 ou moins 0.268062
gpage_mere_naissance30 et plus 0.191557
rang_apres_idealoui 0.602401
z
sexeféminin -4.553
milieu 2.431
richessepauvre -0.328
richessemoyen 1.286
richesseriche 1.184
richessetrès riche 1.084
structureun adulte -0.250
structuredeux adultes de même sexe 1.606
structuretrois adultes ou plus avec lien de parenté 0.251
structureadultes sans lien de parenté -0.430
educ2primaire -0.147
educ2secondaire ou plus -0.556
gpage_mere_naissance19 ou moins -1.157
gpage_mere_naissance30 et plus -0.014
rang_apres_idealoui 2.250
p
sexeféminin 5.29e-06
milieu 0.0151
richessepauvre 0.7427
richessemoyen 0.1986
richesseriche 0.2362
richessetrès riche 0.2784
structureun adulte 0.8024
structuredeux adultes de même sexe 0.1083
structuretrois adultes ou plus avec lien de parenté 0.8016
structureadultes sans lien de parenté 0.6672
educ2primaire 0.8831
educ2secondaire ou plus 0.5784
gpage_mere_naissance19 ou moins 0.2471
gpage_mere_naissance30 et plus 0.9892
rang_apres_idealoui 0.0245

Likelihood ratio test=38.16 on 15 df, p=0.0008546
n= 1584, number of events= 142 De nombreuses variables ne sont pas significatives. Voyons si nous pouvons, avec la fonction step, améliorer notre modèle par minimisation de l’ AIC ou Akaike Information Criterion (voir la section Sélection de modèles du chapitre sur la Régression logistique). mod2 <- step (mod1) Start: AIC=2027.07
Surv(time, deces) ~ sexe + milieu + richesse + structure + educ2 +
gpage_mere_naissance + rang_apres_ideal

Df AIC
- structure 4 2022.0
- richesse 4 2022.7
- educ2 2 2023.4
- gpage_mere_naissance 2 2024.6
<none> 2027.1
- rang_apres_ideal 1 2028.6
- milieu 1 2031.3
- sexe 1 2047.1

Step: AIC=2022.01
Surv(time, deces) ~ sexe + milieu + richesse + educ2 + gpage_mere_naissance +
rang_apres_ideal

Df AIC
- richesse 4 2017.0
- educ2 2 2018.2
- gpage_mere_naissance 2 2019.5
<none> 2022.0
- rang_apres_ideal 1 2023.4
- milieu 1 2025.6
- sexe 1 2042.2

Step: AIC=2017
Surv(time, deces) ~ sexe + milieu + educ2 + gpage_mere_naissance +
rang_apres_ideal

Df AIC
- educ2 2 2013.3
- gpage_mere_naissance 2 2014.8
<none> 2017.0
- rang_apres_ideal 1 2018.0
- milieu 1 2018.8
- sexe 1 2037.6

Step: AIC=2013.28
Surv(time, deces) ~ sexe + milieu + gpage_mere_naissance + rang_apres_ideal

Df AIC
- gpage_mere_naissance 2 2011.2
<none> 2013.3
- rang_apres_ideal 1 2014.3
- milieu 1 2015.7
- sexe 1 2033.8

Step: AIC=2011.17
Surv(time, deces) ~ sexe + milieu + rang_apres_ideal

Df AIC
<none> 2011.2
- rang_apres_ideal 1 2012.3
- milieu 1 2013.9
- sexe 1 2031.6 On peut obtenir facilement les coefficients du modèle avec l’excellente fonction tidy de l’extension broom . Ne pas oublier de préciser exponentiate = TRUE . En effet, dans le cas d’un modèle de Cox, l’exponentiel des coefficients corresponds au ratio des risques instantannés ou hazard ratio (HR) en anglais. library (broom, quietly = TRUE) tidy (mod2, exponentiate = TRUE) {"columns":[{"label":["term"],"name":[1],"type":["chr"],"align":["left"]},{"label":["estimate"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["std.error"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["statistic"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["p.value"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["conf.low"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["conf.high"],"name":[7],"type":["dbl"],"align":["right"]}],"data":[{"1":"sexeféminin","2":"0.4429992","3":"0.1774811","4":"-4.587459","5":"4.486738e-06","6":"0.3128461","7":"0.6272998"},{"1":"milieu","2":"1.5206198","3":"0.1987723","4":"2.108533","5":"3.498491e-02","6":"1.0299718","7":"2.2449981"},{"1":"rang_apres_idealoui","2":"3.4410694","3":"0.5840690","4":"2.115816","5":"3.436049e-02","6":"1.0952998","7":"10.8107009"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Pour représenter ces rapports de risque, on peut ici encore avoir recours à la fonction ggcoef de l’extension GGally . library (GGally, quietly = TRUE) Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2 Attaching package: 'GGally' The following object is masked from 'package:dplyr':

nasa ggcoef (mod2, exponentiate = TRUE) Coefficients du modèle avec ggcoef L’extension survminer fournit également une fonction ggforest qui permet de représenter de manière plus esthétique et complète les coefficients d’un modèle de Cox. ggforest (mod2) Coefficients du modèle avec ggforest Vérification de la validité du modèle Un modèle de Cox n’est valable que sous l’hypothèse de la proportionnalité des risques relatifs. Selon cette hypothèse les résidus de Schoenfeld ne dépendent pas du temps. Cette hypothèse peut être testée avec la fonction cox.zph . test <- cox.zph (mod2) test chisq df p
sexe 0.505 1 0.48
milieu 0.136 1 0.71
rang_apres_ideal 0.163 1 0.69
GLOBAL 0.805 3 0.85 Une valeur de p inférieure à 5 % indique que l’hypothèse n’est pas vérifiée. Il apparaît que p est supérieur à 5 % globalement et pour chaque variable prise individuellement. Notre modèle est donc valide. Il est possible de représenter la distribution des résidus de Schoenfeld à l’aide de ggcoxzph de l’extension survminer, afin de voir si leur répartition change au cours du temps. ggcoxzph (test) Warning in regularize.values(x, y, ties, missing(ties),
na.rm = na.rm): collapsing to unique 'x' values Résidus de Schoenfeld Données pondérées Si vous utilisez des données pondérées avec un plan d’échantillonnage complexe (voir le chapitre dédié), vous pouvez utilisez les fonctions suivantes de l’extension survey : svykm pour estimer une courbe de survie de Kaplan-Meier ; svycoxph pour un modèle de Cox. Dans les deux cas, pensez à ajouter l’option se = TRUE pour que les erreurs standards soient calculées (et que les intervalles de confiance puissent être générés). Ici, pour plus de simplicité, nous n’avons pas pris en compte les décès éventuels des enfants de rang inférieur avant la naissance considérée. Si cela n’avait pas été le cas, nous aurions eu recours à la fonction tbl_df . Ici, pour plus de simplicité, nous n’avons pas pris en compte les décès éventuels des enfants de rang inférieur avant la naissance considérée. Analyse de séquences L’analyse de séquences Charger TraMineR et récupérer les données Appariement optimal et classification Représentations graphiques Distribution de la typologie Pour aller plus loin Bibliographie La version originale de ce chapitre est une reprise, avec l’aimable autorisation de son auteur, d’un article de Nicolas Robette intitulé L’analyse de séquences : une introduction avec le logiciel R et le package TraMineR et publié sur le blog Quanti (http://quanti.hypotheses.org/686/). Depuis les années 1980, l’étude quantitative des trajectoires biographiques (life course analysis) a pris une ampleur considérable dans le champ des sciences sociales. Les collectes de données micro-individuelles longitudinales se sont développées, principalement sous la forme de panels ou d’enquêtes rétrospectives. Parallèlement à cette multiplication des données disponibles, la méthodologie statistique a connu de profondes évolutions. L’ analyse des biographies (event history analysis) — qui ajoute une dimension diachronique aux modèles économétriques mainstream — s’est rapidement imposée comme l’approche dominante : il s’agit de modéliser la durée des situations ou le risque d’occurrence des événements. L’analyse de séquences Cependant, ces dernières années ont vu la diffusion d’un large corpus de méthodes descriptives d’ analyse de séquences, au sein desquelles l’ appariement optimal (optimal matching) occupe une place centrale 1 . L’objectif principal de ces méthodes est d’identifier — dans la diversité d’un corpus de séquences constituées de séries d’états successifs — les régularités, les ressemblances, puis le plus souvent de construire des typologies de « séquences-types ». L’analyse de séquences constitue donc un moyen de décrire mais aussi de mieux comprendre le déroulement de divers processus. La majeure partie des applications de l’analyse de séquences traite de trajectoires biographiques ou de carrières professionnelles. Dans ces cas, chaque trajectoire ou chaque carrière est décrite par une séquence, autrement dit par une suite chronologiquement ordonnée de « moments » élémentaires, chaque moment correspondant à un « état » déterminé de la trajectoire (par exemple, pour les carrières professionnelles : être en emploi, au chômage ou en inactivité). Mais on peut bien sûr imaginer des types de séquences plus originaux : Andrew Abbott 2, le sociologue américain qui a introduit l’ optimal matching dans les sciences scientifiques ou des séquences de pas de danses traditionnelles. En France, les premiers travaux utilisant l’appariement optimal sont ceux de Claire Lemercier 3 sur les carrières des membres des institutions consulaires parisiennes au xix e siècle (Lemercier, 2005), et de Laurent Lesnard 4 sur les emplois du temps (Lesnard, 2008). Mais dès les années 1980, les chercheurs du Céreq construisaient des typologies de trajectoires d’insertion à l’aide des méthodes d’analyse des données « à la française » (analyse des correspondances, etc.) 5 . Au final, on dénombre maintenant plus d’une centaine d’articles de sciences sociales contenant ou discutant des techniques empruntées à l’analyse de séquences. Pour une présentation des différentes méthodes d’analyse de séquences disponibles et de leur mise en oeuvre pratique, il existe un petit manuel en français, publié en 2011 dernière aux éditions du Ceped (collection « Les clefs pour » 6) et disponible en pdf 7 (Robette, 2011). De plus, un article récemment publié dans le Bulletin de Méthodologie Sociologique compare de manière systématique les résultats obtenus par les principales méthodes d’analyse de séquences (Robette & Bry, 2012). La conclusion en est qu’avec des données empiriques aussi structurées que celles que l’on utilise en sciences sociales, l’approche est robuste, c’est-à-dire qu’un changement de méthode aura peu d’influence sur les principaux résultats. Cependant, l’article tente aussi de décrire les spécificités de chaque méthode et les différences marginales qu’elles font apparaître, afin de permettre aux chercheurs de mieux adapter leurs choix méthodologiques à leur question de recherche. Afin d’illustrer la démarche de l’analyse de séquences, nous allons procéder ici à la description « pas à pas » d’un corpus de carrières professionnelles, issues de l’enquête Biographies et entourage (Ined, 2000) 8 . Et pour ce faire, on va utiliser le logiciel R, qui propose la solution actuellement la plus complète et la plus puissante en matière d’analyse de séquences. Les méthodes d’analyse de séquences par analyses factorielles ou de correspondances ne nécessitent pas de logiciel spécifique : tous les logiciels de statistiques généralistes peuvent être utilisés (SAS, SPSS, Stata, R, etc.). En revanche, il n’existe pas de fonctions pour l’appariement optimal dans SAS ou SPSS . Certains logiciels gratuits implémentent l’appariement optimal (comme Chesa 9 ou TDA 10) mais il faut alors recourir à d’autres programmes pour dérouler l’ensemble de l’analyse (classification, représentation graphique). Stata propose le module sq 11, qui dispose d’un éventail de fonctions intéressantes. Mais c’est R et le package TraMineR 12, développé par des collègues de l’Université de Genève (Gabadinho et al, 2011), qui fournit la solution la plus complète et la plus puissante à ce jour : on y trouve l’appariement optimal mais aussi d’autres algorithmes alternatifs, ainsi que de nombreuses fonctions de description des séquences et de représentation graphique. Charger TraMineR et récupérer les données Tout d’abord, à quoi ressemblent nos données ? On a reconstruit à partir de l’enquête les carrières de 1000 hommes. Pour chacune, on connaît la position professionnelle chaque année, de l’âge de 14 ans jusqu’à 50 ans. Cette position est codée de la manière suivante : les codes 1 à 6 correspondent aux groupes socioprofessionnels de la nomenclature des PCS de l’INSEE 13 (agriculteurs exploitants ; artisans, commerçants et chefs d’entreprise ; cadres et professions intellectuelles supérieures ; professions intermédiaires ; employés ; ouvriers) ; on y a ajouté « études » (code 7), « inactivité » (code 8) et « service militaire » (code 9). Le fichier de données comporte une ligne par individu et une colonne par année : la variable csp1 correspond à la position à 14 ans, la variable csp2 à la position à 15 ans, etc. Par ailleurs, les enquêtés étant tous nés entre 1930 et 1950, on ajoute à notre base une variable « génération » à trois modalités, prenant les valeurs suivantes : 1=“1930-1938” ; 2=“1939-1945” ; 3=“1946-1950”. Au final, la base est constituée de 500 lignes et de 37 + 1 = 38 colonnes et se présente sous la forme d’un fichier texte au format csv (téléchargeable à http://larmarange.github.io/analyse-R/data/trajpro.csv). Une fois R ouvert, on commence par installer les extensions nécessaires à ce programme (opération à ne réaliser que lors de leur première utilisation) et par les charger en mémoire. L’extension TraMineR propose de nombreuses fonctions pour l’analyse de séquences. L’extension cluster comprend un certain nombre de méthodes de classification automatique 13 . library (TraMineR) TraMineR stable version 2.2-0 (Built: 2020-04-27) Website: http://traminer.unige.ch Please type 'citation("TraMineR")' for citation information. library (cluster) On importe ensuite les données, on recode la variable « génération » pour lui donner des étiquettes plus explicites. On jette également un coup d’oeil à la structure du tableau de données : Parsed with column specification:
cols(
.default = col_double()
) See spec(...) for full column specifications. donnees <- read.csv ("http://larmarange.github.io/analyse-R/data/trajpro.csv", header = T) donnees $ generation <- factor (donnees $ generation, labels = c ("1930-38", "1939-45", "1946-50")) str (donnees) tibble [1,000 x 38] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ csp1 : num [1:1000] 1 7 6 7 7 6 7 7 7 6 ...
$ csp2 : num [1:1000] 1 7 6 7 7 6 7 7 6 6 ...
$ csp3 : num [1:1000] 1 7 6 6 7 6 7 7 6 6 ...
$ csp4 : num [1:1000] 1 7 6 6 7 6 7 7 6 6 ...
$ csp5 : num [1:1000] 1 7 6 6 7 6 7 7 6 6 ...
$ csp6 : num [1:1000] 1 7 6 6 7 6 9 7 6 6 ...
$ csp7 : num [1:1000] 6 9 6 6 7 6 9 7 9 6 ...
$ csp8 : num [1:1000] 6 9 9 6 7 6 9 7 4 6 ...
$ csp9 : num [1:1000] 6 6 9 6 7 6 9 3 4 9 ...
$ csp10 : num [1:1000] 6 6 9 6 7 6 4 3 4 9 ...
$ csp11 : num [1:1000] 6 6 6 6 3 6 4 3 4 6 ...
$ csp12 : num [1:1000] 6 6 6 6 3 6 4 3 4 6 ...
$ csp13 : num [1:1000] 6 6 6 6 3 6 4 3 4 6 ...
$ csp14 : num [1:1000] 6 4 6 6 3 6 4 3 4 6 ...
$ csp15 : num [1:1000] 6 4 6 6 3 6 4 3 4 6 ...
$ csp16 : num [1:1000] 6 4 6 6 3 6 6 3 4 6 ...
$ csp17 : num [1:1000] 6 4 6 6 3 6 6 3 4 6 ...
$ csp18 : num [1:1000] 6 4 6 6 3 6 6 3 4 6 ...
$ csp19 : num [1:1000] 6 4 6 6 3 6 6 3 4 6 ...
$ csp20 : num [1:1000] 6 4 6 6 3 6 6 3 4 6 ...
$ csp21 : num [1:1000] 6 4 6 6 6 6 6 3 4 6 ...
$ csp22 : num [1:1000] 6 4 6 6 6 6 6 3 4 4 ...
$ csp23 : num [1:1000] 6 4 6 6 6 6 6 3 4 4 ...
$ csp24 : num [1:1000] 6 6 6 6 5 6 6 3 4 4 ...
$ csp25 : num [1:1000] 6 6 6 6 5 6 6 3 4 4 ...
$ csp26 : num [1:1000] 6 6 6 6 5 6 6 3 4 4 ...
$ csp27 : num [1:1000] 6 6 6 6 5 6 6 3 4 4 ...
$ csp28 : num [1:1000] 6 6 6 6 5 6 6 3 4 4 ...
$ csp29 : num [1:1000] 6 6 6 6 5 6 6 3 4 4 ...
$ csp30 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp31 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp32 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp33 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp34 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp35 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp36 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ csp37 : num [1:1000] 4 6 6 6 5 6 6 3 4 4 ...
$ generation: Factor w/ 3 levels "1930-38","1939-45",..: 2 1 1 3 2 3 1 1 2 1 ...
- attr(*, "spec")=
.. cols(
.. csp1 = col_double(),
.. csp2 = col_double(),
.. csp3 = col_double(),
.. csp4 = col_double(),
.. csp5 = col_double(),
.. csp6 = col_double(),
.. csp7 = col_double(),
.. csp8 = col_double(),
.. csp9 = col_double(),
.. csp10 = col_double(),
.. csp11 = col_double(),
.. csp12 = col_double(),
.. csp13 = col_double(),
.. csp14 = col_double(),
.. csp15 = col_double(),
.. csp16 = col_double(),
.. csp17 = col_double(),
.. csp18 = col_double(),
.. csp19 = col_double(),
.. csp20 = col_double(),
.. csp21 = col_double(),
.. csp22 = col_double(),
.. csp23 = col_double(),
.. csp24 = col_double(),
.. csp25 = col_double(),
.. csp26 = col_double(),
.. csp27 = col_double(),
.. csp28 = col_double(),
.. csp29 = col_double(),
.. csp30 = col_double(),
.. csp31 = col_double(),
.. csp32 = col_double(),
.. csp33 = col_double(),
.. csp34 = col_double(),
.. csp35 = col_double(),
.. csp36 = col_double(),
.. csp37 = col_double(),
.. generation = col_double()
.. ) On a bien 1000 observations et 38 variables. On définit maintenant des labels pour les différents états qui composent les séquences et on crée un objet « séquence » avec seqdef : labels <- c ("agric", "acce", "cadr", "pint", "empl", "ouvr", "etud", "inact", "smil") seq <- seqdef (donnees[, 1 : 37], states = labels) [>] state coding: [alphabet] [label] [long label] 1 1 agric agric 2 2 acce acce 3 3 cadr cadr 4 4 pint pint 5 5 empl empl 6 6 ouvr ouvr 7 7 etud etud 8 8 inact inact 9 9 smil smil [>] 1000 sequences in the data set [>] min/max sequence length: 37/37 Appariement optimal et classification Ces étapes préalables achevées, on peut comparer les séquences en calculant les dissimilarités entre paires de séquences. On va ici utiliser la méthode la plus répandue, l’appariement optimal (optimal matching). Cette méthode consiste, pour chaque paire de séquences, à compter le nombre minimal de modifications (substitutions, suppressions, insertions) qu’il faut faire subir à l’une des séquences pour obtenir l’autre. On peut considérer que chaque modification est équivalente, mais il est aussi possible de prendre en compte le fait que les « distances » entre les différents états n’ont pas toutes la même « valeur » (par exemple, la distance sociale entre emploi à temps plein et chômage est plus grande qu’entre emploi à temps plein et emploi à temps partiel), en assignant aux différentes modifications des « coûts » distincts. Dans notre exemple, on va créer avec seqsubm une « matrice des coûts de substitution » dans laquelle tous les coûts sont constants et égaux à 2 14 : couts <- seqsubm (seq, method = "CONSTANT", cval = 2) [>] creating 9x9 substitution-cost matrix using 2 as constant value Ensuite, on calcule la matrice de distances entre les séquences (i.e contenant les « dissimilarités » entre les séquences) avec seqdist, avec un coût d’insertion/suppression (indel) que l’on fixe ici à 1 : seq.om <- seqdist (seq, method = "OM", indel = 1, sm = couts) [>] 1000 sequences with 9 distinct states [>] checking 'sm' (size and triangle inequality) [>] 818 distinct sequences [>] min/max sequence lengths: 37/37 [>] computing distances using the OM metric [>] elapsed time: 1.6 secs Ce cas de figure où tous les coûts de substitution sont égaux à 2 et le coût indel égal à 1 correspond à un cas particulier d’ optimal matching que l’on appelle la Longuest Common Subsequence ou LCS . Elle peut se calculer directement avec seqdist de la manière suivante : seq.om <- seqdist (seq, method = "LCS") [>] 1000 sequences with 9 distinct states [>] creating a 'sm' with a substitution cost of 2 [>] creating 9x9 substitution-cost matrix using 2 as constant value [>] 818 distinct sequences [>] min/max sequence lengths: 37/37 [>] computing distances using the LCS metric [>] elapsed time: 2.25 secs En l’absence d’hypothèses fortes sur les différents statuts auxquels correspond notre alphabet (données hiérarchisées, croisement de différentes dimensions\dots), nous vous recommandons d’utiliser prioritairement la métrique LCS pour calculer la distance entre les séquences. On pourra trouver un exemple de matrice de coûts hiérarchisée dans le chapitre sur les trajectoires de soins . Cette matrice des distances ou des dissimilarités entre séquences peut ensuite être utilisée pour une classification ascendante hiérarchique (CAH), qui permet de regrouper les séquences en un certain nombre de « classes » en fonction de leur proximité : seq.dist <- hclust (as.dist (seq.om), method = "ward.D2") Avec la fonction plot, il est possible de tracer l’arbre de la classification (dendrogramme). plot (as.dendrogram (seq.dist), leaflab = "none") Dendrogramme de la classification des séquences De même, on peut représenter les sauts d’inertie . plot (sort (seq.dist $ height, decreasing = TRUE)[1 : 20], type = "s", xlab = "nb de classes", ylab = "inertie") Sauts d’inertie de la classification des séquences L’observation, sur ce dendogramme ou sur la courbe des sauts d’inertie, des sauts d’inertie des dernières étapes de la classification peut servir de guide pour déterminer le nombre de classes que l’on va retenir pour la suite des analyses. Une première inflexion dans la courbe des sauts d’inertie apparaît au niveau d’une partition en 5 classes. On voit aussi une seconde inflexion assez nette à 7 classes. Mais il faut garder en tête le fait que ces outils ne sont que des guides, le choix devant avant tout se faire après différents essais, en fonction de l’intérêt des résultats par rapport à la question de recherche et en arbitrant entre exhaustivité et parcimonie. On fait ici le choix d’une partition en 5 classes : nbcl <- 5 seq.part <- cutree (seq.dist, nbcl) seq.part <- factor (seq.part, labels = paste ("classe", 1 : nbcl, sep = ".")) Représentations graphiques Pour se faire une première idée de la nature des classes de la typologie, il existe un certain nombre de représentations graphiques. Les chronogrammes (state distribution plots) présentent une série de coupes transversales : pour chaque âge, on a les proportions d’individus de la classe dans les différentes situations (agriculteur, étudiant, etc.). Ce graphique s’obtient avec seqdplot : seqdplot (seq, group = seq.part, xtlab = 14 : 50, border = NA) Chronogrammes Chacune des classes semble caractérisée par un groupe professionnel principal : profession intermédiaire pour la classe 1, ouvrier pour la 2, employé pour la 3, cadre pour la 4 et indépendant pour la 5. Cependant, on aperçoit aussi des « couches » d’autres couleurs, indiquant que l’ensemble des carrières ne sont probablement pas stables. Les « tapis » (index plots), obtenus avec seqIplot, permettent de mieux visualiser la dimension individuelle des séquences. Chaque segment horizontal représente une séquence, découpée en sous-segments correspondant aux aux différents états successifs qui composent la séquence. seqIplot (seq, group = seq.part, xtlab = 14 : 50, space = 0, border = NA, yaxis = FALSE) Tapis des séquences triés Il est possible de trier les séquences pour rendre les tapis plus lisibles (on trie ici par multidimensional scaling à l’aide de la fonction cmdscale). ordre <- cmdscale (as.dist (seq.om), k = 1) seqIplot (seq, group = seq.part, sortv = ordre, xtlab = 14 : 50, space = 0, border = NA, yaxis = FALSE) Tapis des séquences triés par multidimensional scaling On voit mieux apparaître ainsi l’hétérogénéité de certaines classes. Les classes 1, 3 et 4, par exemple, semblent regrouper des carrières relativement stables (respectivement de professions intermédiaires, d’employés et de cadres) et des carrières plus « mobiles » commencées comme ouvrier (classes 1 et 3, en orange) ou comme profession intermédiaire (classe 4, en rouge). De même, la majorité des membres de la dernière classe commencent leur carrière dans un groupe professionnel distinct de celui qu’ils occuperont par la suite (indépendants). Ces distinctions apparaissent d’ailleurs si on relance le programme avec un nombre plus élevé de classes (en remplaçant le 5 de la ligne nbcl <- 5 par 7, seconde inflexion de la courbe des sauts d’inertie, et en exécutant de nouveau le programme à partir de cette ligne) : les stables et les mobiles se trouvent alors dans des classes distinctes. Le package JLutils, disponible sur GitHub, propose une fonction seq_heatmap permettant de représenter le tapis de l’ensemble des séquences selon l’ordre du dendrogramme. Pour installer JLutils, on aura recours au package devtools et à sa fonction install_github : library (devtools) install_github ("larmarange/JLutils") On peut ensuite générer le graphique ainsi : library (JLutils) Loading required package: ggplot2 Loading required package: plyr Loading required package: magrittr seq_heatmap (seq, seq.dist, labCol = 14 : 50) Tapis des séquences trié selon le dendrogramme Il est possible de reproduire un tapis de séquence avec ggplot2 . Outre le fait que cela fournit plus d’options de personnalisation du graphique, cela permets également à ce que la hauteur de chaque classe sur le graphique soit proportionnelle aux nombre d’invidus. En premier lieu, on a va ajouter à notre fichier de données des identifiants individuels, la typologie crée et l’ordre obtenu par multidimensional scaling . donnees $ id <- row.names (donnees) donnees $ classe <- seq.part donnees $ ordre <- rank (ordre, ties.method = "random") Ensuite, il est impératif que nos données soient dans un format long et tidy, c’est-à-dire avec une ligne par individu et par pas de temps. Pour cela on aura recours à la fonction gather (voir le chapitre dédié). library (tidyr) Attaching package: 'tidyr' The following object is masked from 'package:magrittr':

extract long <- donnees %>% gather (csp1 : csp37, key = annee, value = csp) On va mettre en forme la variable csp sous forme de facteur, récupérer l’année grace à la fonction str_sub de l’extension stringr (voir le chapitre sur la manipulation de texte) et recalculer l’âge. long $ csp <- factor (long $ csp, labels = c ("agriculteur", "art./com./chefs", "cadres", "prof. int.", "employés", "ouvriers", "étudiants", "inactifs", "serv. militaire")) library (stringr) long $ annee <- as.integer (str_sub (long $ annee, 4)) long $ age <- long $ annee + 13 Il n’y a plus qu’à faire notre graphique grace à geom_raster qui permet de colorier chaque pixel. Techniquement, pour un tapis de séquence, il s’agit de représenter le temps sur l’axe horizontal et les individus sur l’axe vertical. Petite astuce : plutôt que d’utiliser id pour l’axe vertical, nous utilisons ordre afin de trier les observations. Par ailleurs, il est impératif de transformer au passage ordre en facteur afin que ggplot2 puisse recalculer proprement et séparément les axes pour chaque facette 15, à condition de ne pas oublier l’option scales = "free_y" dans l’appel à facet_grid . Les autres commandes ont surtout pour vocation d’améliorer le rendu du graphique (voir le chapitre dédié à ggplot2). library (ggplot2) ggplot (long) + aes (x = age, y = factor (ordre), fill = csp) + geom_raster () + ylab ("") + scale_y_discrete (label = NULL) + theme_bw () + theme (legend.position = "bottom") + scale_fill_brewer (palette = "Set3") + facet_grid (classe ~ ., scales = "free_y", space = "free_y") + scale_x_continuous (limits = c (14, 50), breaks = c (14, 20, 25, 30, 35, 40, 45, 50), expand = c (0, 0)) Warning: Removed 2000 rows containing missing values
(geom_raster). La distance des séquences d’une classe au centre de cette classe, obtenue avec disscenter, permet de mesurer plus précisément l’ homogénéité des classes . Nous utilisons ici aggregate pour calculer la moyenne par classe : aggregate (disscenter (as.dist (seq.om), group = seq.part), list (seq.part), mean) {"columns":[{"label":["Group.1"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["x"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"classe.1","2":"15.797993"},{"1":"classe.2","2":"6.791962"},{"1":"classe.3","2":"15.032359"},{"1":"classe.4","2":"7.352675"},{"1":"classe.5","2":"14.219915"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Cela nous confirme que les classes 1, 3 et 5 sont nettement plus hétérogènes que les autres, alors que la classe 2 est la plus homogène. D’autres représentations graphiques existent pour poursuivre l’examen de la typologie. On peut visualiser les 10 séquences les plus fréquentes de chaque classe avec seqfplot . seqfplot (seq, group = seq.part) Séquences les plus fréquentes de chaque classe On peut aussi visualiser avec seqmsplot l’état modal (celui qui correspond au plus grand nombre de séquences de la classe) à chaque âge. seqmsplot (seq, group = seq.part, xtlab = 14 : 50, main = "classe") Statut modal à chaque âge On peut également représenter avec seqmtplot les durées moyennes passées dans les différents états. seqmtplot (seq, group = seq.part) Durée moyenne dans chaque statut La fonction seqrplot cherche à identifier des séquences représentatives de chaque classe. Plusieurs méthodes sont proposées (voir seqrep). La méthode dist cherche à identifier des séquences centrales à chaque classe, c’est-à-dire situées à proximité du centre de la classe. Selon l’hétérogénéité de la classe, plusieurs séquences représentatives peuvent être renvoyées. ATTENTION : il faut être prudent dans l’interprétation de ces séquences centrales de la classe dans la mesure où elles ne rendent pas toujours compte de ce qui se passe dans la classe et où elles peuvent induire en erreur quand la classe est assez hétérogène. Il faut donc les considérer tout en ayant en tête l’ensemble du tapis de séquence pour voir si elles sont effectivement de bonnes candidates. seqrplot (seq, group = seq.part, dist.matrix = seq.om, criterion = "dist") Séquences représentatives de chaque classe Enfin, l’ entropie transversale décrit l’évolution de l’homogénéité de la classe. Pour un âge donné, une entropie proche de 0 signifie que tous les individus de la classe (ou presque) sont dans la même situation. À l’inverse, l’entropie est de 1 si les individus sont dispersés dans toutes les situations. Ce type de graphique produit par seqHtplot peut être pratique pour localiser les moments de transition, l’insertion professionnelle ou une mobilité sociale ascendante. seqHtplot (seq, group = seq.part, xtlab = 14 : 50) Entropie transversale Distribution de la typologie On souhaite maintenant connaître la distribution de la typologie (en effectifs et en pourcentages) : library (questionr) freq (seq.part) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["n"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["%"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["val%"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"366","2":"36.6","3":"36.6","_rn_":"classe.1"},{"1":"183","2":"18.3","3":"18.3","_rn_":"classe.2"},{"1":"104","2":"10.4","3":"10.4","_rn_":"classe.3"},{"1":"296","2":"29.6","3":"29.6","_rn_":"classe.4"},{"1":"51","2":"5.1","3":"5.1","_rn_":"classe.5"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} On poursuit ensuite la description des classes en croisant la typologie avec la variable generation : cprop (table (seq.part, donnees $ generation)) seq.part 1930-38 1939-45 1946-50 Ensemble
classe.1 35.6 32.5 40.8 36.6
classe.2 19.7 18.3 17.0 18.3
classe.3 6.5 13.9 11.2 10.4
classe.4 31.8 29.2 27.9 29.6
classe.5 6.5 6.1 3.0 5.1
Total 100.0 100.0 100.0 100.0 chisq.test (table (seq.part, donnees $ generation)) Pearson's Chi-squared test

data: table(seq.part, donnees$generation)
X-squared = 18.518, df = 8, p-value = 0.01766 Le lien entre le fait d’avoir un certain type de carrières et la cohorte de naissance est significatif à un seuil de 15 %. On constate par exemple l’augmentation continue de la proportion de carrières de type « professions intermédiaires » (classe 1) et, entre les deux cohortes les plus anciennes, l’augmentation de la part des carrières de type « employés » (classe 3) et la baisse de la part des carrières de type « cadres » (classe 4). Bien d’autres analyses sont envisageables : croiser la typologie avec d’autres variables (origine sociale, etc.), construire l’espace des carrières possibles, étudier les interactions entre trajectoires familiales et professionnelles, analyser la variance des dissimilarités entre séquences en fonction de plusieurs variables « explicatives 16 »\dots Mais l’exemple proposé est sans doute bien suffisant pour une première introduction ! Pour aller plus loin En premier lieu, la lecture du manuel d’utilisation de TraMineR, intitulé Mining sequence data in R with the TraMineR package: A user’s guide et écrit par Alexis Gabadinho, Gilbert Ritschard, Matthias Studer et Nicolas S. Muller, est fortement conseillée. Ce manuel ne se contente pas de présenter l’extension, mais aborde également la théorie sous-jacente de l’analyse de séquences, les différents formats de données, les différences approches (séquences de statut ou séquences de transitions par exemple), etc. Pour une initiation en français, on pourra se référer à l’ouvrage de Nicolas Robette Explorer et décrire les parcours de vie : les typologies de trajectoires sorti en 2011 aux éditions du Ceped. L’extension WeightedCluster de Matthias Studer est un excellent complément à TraMineR . Il a également écrit un manuel de la librairie WeightedCluster : un guide pratique pour la création de typologies de trajectoires en sciences sociales avec R . Enfin, l’extension TraMineRextras (https://cran.r-project.org/package=TraMineRextras) contient des fonctions complémentaires à TraMineR, plus ou moins en phase de test. Bibliographie Abbott A., 2001, Time matters. On theory and method, The University of Chicago Press. Abbott A., Hrycak A., 1990, « Measuring ressemblance in sequence data: an optimal matching analysis of musicians’ careers», American journal of sociology, (96), p.144-185. http://www.jstor.org/stable/10.2307/2780695 Abbott A., Tsay A., 2000, « Sequence analysis and optimal matching methods in sociology: Review and prospect », Sociological methods & research, 29(1), p.3-33. http://smr.sagepub.com/content/29/1/3.short Gabadinho, A., Ritschard, G., Müller, N.S. & Studer, M., 2011, « Analyzing and visualizing state sequences in R with TraMineR », Journal of Statistical Software, 40(4), p.1-37. http://archive-ouverte.unige.ch/downloader/vital/pdf/tmp/4hff8pe6uhukqiavvgaluqmjq2/out.pdf Grelet Y., 2002, « Des typologies de parcours. Méthodes et usages », Document Génération 92, (20), 47 p. http://www.cmh.greco.ens.fr/programs/Grelet_typolparc.pdf Lelièvre É., Vivier G., 2001, « Évaluation d’une collecte à la croisée du quantitatif et du qualitatif : l’enquête Biographies et entourage », Population, (6), p.1043-1073. http://www.persee.fr/web/revues/home/prescript/article/pop_0032-4663_2001_num_56_6_7217 Lemercier C., 2005, « Les carrières des membres des institutions consulaires parisiennes au XIX e siècle », Histoire et mesure, XX (1-2), p.59-95. http://histoiremesure.revues.org/786 Lesnard L., 2008, « Off-Scheduling within Dual-Earner Couples: An Unequal and Negative Externality for Family Time », American Journal of Sociology, 114(2), p.447-490. http://laurent.lesnard.free.fr/IMG/pdf/lesnard_2008_off-scheduling_within_dual-earner_couples-2.pdf Lesnard L., Saint Pol T. (de), 2006, « Introduction aux Méthodes d’Appariement Optimal (Optimal Matching Analysis) », Bulletin de Méthodologie Sociologique, 90, p.5-25. http://bms.revues.org/index638.html Robette N., 2011, Explorer et décrire les parcours de vie : les typologies de trajectoires, Ceped (Les Clefs pour), 86 p. http://nicolas.robette.free.fr/Docs/Robette2011_Manuel_TypoTraj.pdf Robette N., 2012, « Du prosélytisme à la sécularisation. Le processus de diffusion de l’Optimal Matching Analysis », document de travail . http://nicolas.robette.free.fr/Docs/Proselytisme_secularisation_NRobette.pdf Robette N., Bry X., 2012, « Harpoon or bait? A comparison of various metrics to fish for life course patterns », Bulletin de Méthodologie Sociologique, 116, p.5-24. http://nicolas.robette.free.fr/Docs/Harpoon_maggot_RobetteBry.pdf Robette N., Thibault N., 2008, « L’analyse exploratoire de trajectoires professionnelles : analyse harmonique qualitative ou appariement optimal ? », Population, 64(3), p.621-646. http://www.cairn.info/revue-population-2008-4-p-621.htm Savage M., 2009, « Contemporary Sociology and the Challenge of Descriptive Assemblage », European Journal of Social Theory, 12(1), p.155-174. http://est.sagepub.com/content/12/1/155.short Pour une analyse des conditions sociales de la diffusion de l’analyse de séquences dans le champ des sciences sociales, voir Robette, 2012. http://home.uchicago.edu/~aabbott/ http://lemercier.ouvaton.org/document.php?id=62 http://laurent.lesnard.free.fr/article.php3?id_article=22 Voir par exemple l’article d’Yvette Grelet (2002). http://www.ceped.org/?rubrique57 http://nicolas.robette.free.fr/Docs/Robette2011_Manuel_TypoTraj.pdf Pour une analyse plus poussée de ces données, avec deux méthodes différentes, voir Robette & Thibault, 2008. Pour une présentation de l’enquête, voir Lelièvre & Vivier, 2001. http://home.fsw.vu.nl/ch.elzinga/ http://steinhaus.stat.ruhr-uni-bochum.de/tda.html http://www.stata-journal.com/article.html?article=st0111 http://mephisto.unige.ch/traminer/ Pour une présentation plus détaillée, voir le chapitre sur la classification ascendante hiérarchique (CAH) . Le fonctionnement de l’algorithme d’appariement optimal — et notamment le choix des coûts — est décrit dans le chapitre 9 du manuel de TraMineR (http://mephisto.unige.ch/pub/TraMineR/doc/TraMineR-Users-Guide.pdf). Essayez le même code mais avec y = ordre au lieu de y = factor(ordre) et vous comprendrez tout l’intérêt de cette astuce. L’articulation entre méthodes « descriptives » et méthodes « explicatives » est un prolongement possible de l’analyse de séquences. Cependant, l’analyse de séquences était envisagée par Abbott comme une alternative à la sociologie quantitative mainstream, i.e le « paradigme des variables » et ses hypothèses implicites souvent difficilement tenables (Abbott, 2001). Une bonne description solidement fondée théoriquement vaut bien des « modèles explicatifs » (Savage, 2009). Trajectoires de soins : un exemple de données longitudinales Première description des données Évolution de la cascade de soins au cours du temps Analyse de survie classique Une première analyse de séquences sur l’ensemble du fichier Une seconde analyse de séquences limitées aux 18 premiers mois Facteurs associés à l’appartenance à chaque groupe Modèle mixte à classes latentes Modèle à observations répétées Modèle de survie multi-états .section{opacity: 1 !important;} /* contrer mermaid */ Dans ce chapitre, nous allons aborder plusieurs méthodes d’analyse à partir d’un jeu de données longitudinales. Tout d’abord, importons les données dans R avec la commande suivante : load (url ("http://larmarange.github.io/analyse-R/data/care_trajectories.RData")) class (care_trajectories) [1] "data.table" "data.frame" Nous obtenons un objet appelé care_trajectories . La fonction class nous montre qu’il s’agit d’un tableau de données au format data.table (voir le chapitre dédié). Chargeons donc cette extension ainsi que le tidyverse . library (tidyverse, quietly = TRUE) library (data.table, quietly = TRUE) Première description des données Jetons un premier regard aux données. head (care_trajectories) {"columns":[{"label":["id"],"name":[1],"type":["int"],"align":["right"]},{"label":["month"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["care_status"],"name":[3],"type":["S3: haven_labelled"],"align":["right"]},{"label":["sex"],"name":[4],"type":["S3: haven_labelled"],"align":["right"]},{"label":["age"],"name":[5],"type":["S3: haven_labelled"],"align":["right"]},{"label":["education"],"name":[6],"type":["S3: haven_labelled"],"align":["right"]},{"label":["wealth"],"name":[7],"type":["S3: haven_labelled"],"align":["right"]},{"label":["distance_clinic"],"name":[8],"type":["S3: haven_labelled"],"align":["right"]}],"data":[{"1":"3","2":"0","3":"D","4":"1","5":"1","6":"2","7":"2","8":"1"},{"1":"3","2":"1","3":"D","4":"1","5":"1","6":"2","7":"2","8":"1"},{"1":"3","2":"2","3":"D","4":"1","5":"1","6":"2","7":"2","8":"1"},{"1":"3","2":"3","3":"D","4":"1","5":"1","6":"2","7":"2","8":"1"},{"1":"3","2":"4","3":"D","4":"1","5":"1","6":"2","7":"2","8":"1"},{"1":"3","2":"5","3":"D","4":"1","5":"1","6":"2","7":"2","8":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Il apparaît que les données sont dans un format long et tidy (voir le chapitre sur tidyr pour une présentation du concept de tidy data), avec une ligne par individu et par pas de temps. Il apparait également que les données sont stockées sous formes de vecteurs labellisés (voir le chapitre dédié aux vecteurs labellisés). Nous aurons donc besoin de l’extension labelled . library (labelled) Pour une description des variables, on pourra avoir recours à describe de questionr . library (questionr) Attaching package: 'questionr' The following object is masked from 'package:labelled':

lookfor describe (care_trajectories, freq.n.max = 10) [49365 obs. x 8 variables] data.table data.frame

$id: patient identifier
integer: 3 3 3 3 3 3 9 9 13 13 ...
min: 3 - max: 9998 - NAs: 0 (0%) - 2929 unique values

$month: month(s) since diagnosis
numeric: 0 1 2 3 4 5 0 1 0 1 ...
min: 0 - max: 50 - NAs: 0 (0%) - 51 unique values

$care_status: care status
labelled character: "D" "D" "D" "D" "D" "D" "D" "D" "D" "D" ...
NAs: 0 (0%) - 4 unique values
4 value labels: [D] diagnosed, but not in care [C] in care, but not on treatment [T] on treatment, but infection not suppressed [S] on treatment and suppressed infection

n %
[D] diagnosed, but not in care 25374 51.4
[C] in care, but not on treatment 5886 11.9
[T] on treatment, but infection not suppressed 4596 9.3
[S] on treatment and suppressed infection 13509 27.4
Total 49365 100.0

$sex: sex
labelled double: 1 1 1 1 1 1 1 1 0 0 ...
min: 0 - max: 1 - NAs: 0 (0%) - 2 unique values
2 value labels: [0] male [1] female

n %
[0] male 17781 36
[1] female 31584 64
Total 49365 100

$age: age group
labelled double: 1 1 1 1 1 1 2 2 2 2 ...
min: 1 - max: 3 - NAs: 0 (0%) - 3 unique values
3 value labels: [1] 16-29 [2] 30-59 [3] 60+

n %
[1] 16-29 16911 34.3
[2] 30-59 29365 59.5
[3] 60+ 3089 6.3
Total 49365 100.0

$education: education level
labelled double: 2 2 2 2 2 2 3 3 2 2 ...
min: 1 - max: 3 - NAs: 0 (0%) - 3 unique values
3 value labels: [1] primary [2] secondary [3] higher

n %
[1] primary 10417 21.1
[2] secondary 19024 38.5
[3] higher 19924 40.4
Total 49365 100.0

$wealth: wealth group (assets score)
labelled double: 2 2 2 2 2 2 2 2 1 1 ...
min: 1 - max: 3 - NAs: 0 (0%) - 3 unique values
3 value labels: [1] low [2] middle [3] high

n %
[1] low 15432 31.3
[2] middle 20769 42.1
[3] high 13164 26.7
Total 49365 100.0

$distance_clinic: distance to nearest clinic
labelled double: 1 1 1 1 1 1 2 2 2 2 ...
min: 1 - max: 2 - NAs: 0 (0%) - 2 unique values
2 value labels: [1] less than 10 km [2] 10 km or more

n %
[1] less than 10 km 26804 54.3
[2] 10 km or more 22561 45.7
Total 49365 100.0 Dans cette étude, on a suivi des patients à partir du moment où ils ont été diagnostiqués pour une pathologie grave et chronique et on a suivi leurs parcours de soins chaque mois à partir du diagnostic. La variable status contient le statut dans les soins de chaque individu pour chaque mois de suivi : D : s’il n’est pas actuellement suivi dans une clinique, soit que la personne n’est pas encore entrée en clinique après le diagnostic, soit qu’elle a quitté la clinique et qu’elle est donc sortie des soins ; C : indique que le patient est entré en soins (il est suivi dans une clinique) mais il n’a pas encore commencé le traitement, ou bien il a arrêté le traitement mais est toujours suivi en clinique ; T : la personne est sous traitement mais l’infections n’est pas supprimée ou contrôlée, soit que le traitement n’a pas encore eu le temps de faire effet, soit qu’il n’est plus efficace ; S : la personne est suivie en clinique, sous traitement et son infection est supprimée / contrôlée, indiquant que le traitement est efficace et produit son effet. Cette étape ultime du parcours de soins est celle dans laquelle on souhaite maintenir les individus le plus longtemps possible. Il est important de noter que nous avons ici des statuts hiérarchiquement ordonnés (D < C < T < S), ce qui aura son importance pour les choix méthodologiques que nous aurons à faire. Nous disposons également d’autres variables (âge, sexe, niveau d’éducation\dots) qui sont ici dépendantes du temps, c’est-à-dire que le cas échéant, elles peuvent varier d’un mois à l’autre en cas de changement. Avant de démarrer les analyses, françisons certaines de ces variables. var_label (care_trajectories $ sex) <- "Sexe" val_labels (care_trajectories $ sex) <- c (homme = 0, femme = 1) var_label (care_trajectories $ age) <- "Âge" var_label (care_trajectories $ education) <- "Education" val_labels (care_trajectories $ education) <- c (primaire = 1, secondaire = 2, supé rieur = 3) Le fichier contient 49 365 lignes, ce qui ne veut pas dire qu’il y a ce nombre d’invidus suivis au cours du temps, puisque plusieurs lignes correspondent à un même individu. On peut obtenir le nombre d’individus différents assez facilement avec la commande : length (unique (care_trajectories $ id)) [1] 2929 Précision : dans ce fichier, tous les individus ne sont pas suivis pendant la même durée, car ils n’ont pas tous été diagnostiqués au même moment. Cependant, il n’y a pas de trous dans le suivi (ce qui serait le cas si certains individus sortaient de l’observation pendant quelques mois puis re-rentraient dans la cohorte de suivi). Avant d’aller plus avant, il nous faut avoir une idée du nombre d’individus observé au cours du temps, ce que l’on peut obtenir avec : ggplot (care_trajectories) + aes (x = month) + geom_bar () Améliorons ce graphique en y ajoutant la distribution selon le statut dans les soins chaque mois, en améliorant l’axe du temps (tous les 6 mois est plus facile à lire) et en y ajoutant un titre et des étiquettes appropriées. Afin de disposer d’une palette de couleurs à fort contraste, nous allons utiliser l’extension viridis . Enfin, nous allons utiliser une petite astuce pour indiquer les effectifs sur l’axe horizontal. Au passage, nous allons également franciser les étiquettes de la variable care_status avec val_labels (notez aussi le recours à to_factor dans aes qui nous permet de transformer à la volée la variable en facteur, format attendu par ggplot2 pour les variables catégorielles). On se référera au chapitre dédié à ggplot2 pour plus de détails sur les différentes fonctions de cette extension graphique. library (viridis) n <- care_trajectories[month %in% (0 : 8 * 6), .(n = .N), by = month] $ n etiquettes <- paste0 ("M", 0 : 8 * 6, " \n (n=", n, ")") val_labels (care_trajectories $ care_status) <- c ("diagnostiqué, mais pas suivi" = "D", "suivi, mais pas sous traitement" = "C", "sous traitement, mais infection non contrôlée" = "T", "sous traitement et infection contrôlée" = "S") ggplot (care_trajectories) + aes (x = month, fill = to_factor (care_status)) + geom_bar (color = "gray50", width = 1) + scale_x_continuous (breaks = 0 : 8 * 6, labels = etiquettes) + ggtitle ("Distribution du statut dans les soins chaque mois") + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) On s’aperçoit qu’une majorité des personnes suivies ne l’ont été que peu de temps, avec une décroissance rapide des effectifs. Évolution de la cascade de soins au cours du temps On nomme communément cascade de soins la proportion d’individus dans chaque statut à un moment du temps donné. On peut facilement obtenir celle-ci à partir du code du graphique précédent en ajoutant l’option position = fill à geom_bar . ggplot (care_trajectories) + aes (x = month, fill = to_factor (care_status)) + geom_bar (color = "gray50", width = 1, position = "fill") + scale_x_continuous (breaks = 0 : 8 * 6, labels = etiquettes) + scale_y_continuous (labels = scales :: percent) + ggtitle ("Cascade des soins observée, selon le temps depuis le diagnostic") + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) Les effectifs sont très faibles au-delà de 36 mois et il serait préférable de couper la cascade au-delà de M36, ce que l’on peut faire aisément ne gardant que les lignes correspondantes de care_trajectories . casc_obs <- ggplot (care_trajectories[month <= 36]) + aes (x = month, fill = to_factor (care_status)) + geom_bar (color = "gray50", width = 1, position = "fill") + scale_x_continuous (breaks = 0 : 8 * 6, labels = etiquettes) + scale_y_continuous (labels = scales :: percent) + ggtitle ("Cascade des soins observée, selon le temps depuis le diagnostic") + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) casc_obs Analyse de survie classique L’ analyse de survie constitue l’approche statistique la plus fréquente pour appréhender des données biographiques. Dans sa version classique, l’analyse de survie modélise le temps mis pour vivre un événement particulier à partir d’un événement origine. Dans notre exemple, l’événement d’origine commun à tous les individus est le diagnostic VIH. Les personnes suivies peuvent vivre trois événements principaux : entrée en soins (passage de D à C) ; initiation du traitement (passage de C à T) ; contrôle de l’infection (passage de T à S). En toute rigueur, il faudrait également considérer les transitions inverses (sortie de soins, arrêt du traitement, échec virologique). De même, il est possible que certains aient vécus plusieurs transitions successives (entrée en soin, initiation du traitement, sortie de soins et arrêt du traitement, nouvelle entrée en soins\dots). Pour le moment, contentons-nous de regarder la première entrée en soins, la première initiation du traitement et la première atteinte du contrôle de l’infection et de calculer la date de ces trois événements, dans un fichier ind contenant une ligne par individu. ind <- care_trajectories[month == 0] ind $ diagnostic <- 0 ind <- merge (ind, care_trajectories[care_status %in% c ("C", "T", "S"), .(entree_soins = min (month)), by = id], by = "id", all.x = TRUE) ind <- merge (ind, care_trajectories[care_status %in% c ("T", "S"), .(initiation_tt = min (month)), by = id], by = "id", all.x = TRUE) ind <- merge (ind, care_trajectories[care_status == "S", .(controle = min (month)), by = id], by = "id", all.x = TRUE) Il nous faut également la durée de suivi par individu. ind <- merge (ind, care_trajectories[, .(suivi = max (month)), by = id], by = "id", all.x = TRUE) Pour faciliter la suite des analyses, nous allons nous créer une petite fonction qui, en fonction de la date d’origine et la date d’événement retenues, calculera la courbe de Kaplan-Meier correspondante (voir le chapitre sur l’ analyse de suivie pour le calcul de la courbe de survie et celui dédié à l’ écriture de fonctions). km <- function (date_origine, date_evenement, nom) {library (survival) # ne garder que les observations avec date d'origine tmp <- ind[! is.na (ind[[date_origine]]), ] # pre-remplir la variable time avec duree de suivi # depuis date d'origine tmp $ time <- tmp $ suivi - tmp[[date_origine]] # et considérer que l'événement n'a pas été vécu tmp $ event <- FALSE # si date_evement documentée, événement vécu tmp[! is.na (tmp[[date_evenement]]), ] $ event <- TRUE tmp[tmp $ event == TRUE, ] $ time <- tmp[tmp $ event == TRUE, ][[date_evenement]] - tmp[tmp $ event == TRUE, ][[date_origine]] kaplan <- survfit (Surv (time, event) ~ 1, data = tmp) res <- broom :: tidy (kaplan, conf.int = TRUE) res $ nom <- nom res} Une première approche consiste à regarder la survenue de chacun des trois événements mentions plus haut en fonction du temps depuis le diagnostic. depuis_diag <- dplyr :: bind_rows (km ("diagnostic", "entree_soins", "entrée en soins"), km ("diagnostic", "initiation_tt", "initiation du traitement"), km ("diagnostic", "controle", "contrôle de l'infection")) g_diag <- ggplot (data = depuis_diag) + aes (x = time, y = 1 - estimate, color = as_factor (nom), fill = as_factor (nom), ymin = 1 - conf.high, ymax = 1 - conf.low) + geom_ribbon (alpha = .25, mapping = aes (color = NULL)) + geom_line (size = 1) + theme_classic () + theme (legend.position = "bottom", panel.grid.major.y = element_line (colour = "grey")) + scale_x_continuous (breaks = 0 : 6 * 6, limits = c (0, 36)) + scale_y_continuous (labels = scales :: percent, limits = c (0, 1)) + xlab ("mois depuis le diagnostic") + ylab ("") + labs (color = "", fill = "") g_diag Ce graphique ressemble à la cascade des soins observée que nous avions calculée plus haut, à quelques différences près : avec la méthode de Kaplan-Meier, la censure à droite, i.e. le fait que tous les individus n’ont pas la même durée de suivi, est correctement prise en compte et la courbe est corrigée en conséquence ; par contre, les transitions inverses ne sont pas considérées : lorsqu’un individu a atteint une étape, on ne regarde pas s’il en ressort. Une autre manière d’appréhender nos trajectoires est de considérer le temps requis pour atteindre une étape une fois la précédente étape atteinte. Ce qu’on obtient facilement en adaptant légèrement notre code précédent. depuis_prec <- dplyr :: bind_rows (km ("diagnostic", "entree_soins", "entrée en soins"), km ("entree_soins", "initiation_tt", "initiation du traitement"), km ("initiation_tt", "controle", "contrôle de l'infection")) g_prec <- ggplot (data = depuis_prec) + aes (x = time, y = 1 - estimate, color = as_factor (nom), fill = as_factor (nom), ymin = 1 - conf.high, ymax = 1 - conf.low) + geom_ribbon (alpha = .25, mapping = aes (color = NULL)) + geom_line (size = 1) + theme_classic () + theme (legend.position = "bottom", panel.grid.major.y = element_line (colour = "grey")) + scale_x_continuous (breaks = 0 : 6 * 6, limits = c (0, 36)) + scale_y_continuous (labels = scales :: percent, limits = c (0, 1)) + xlab ("mois depuis l'étape précédente") + ylab ("") + labs (color = "", fill = "") g_prec Attention : cette représentation graphique peut éventuellement prêter à confusion dans sa lecture car l’échelle de temps n’est pas tout à fait la même pour chaque courbe, dans la mesure où la date d’origine diffère pour chacune. Dès lors, il peut être plus pertinent de présenter chaque courbe l’une à côté de l’autre. g_prec + facet_grid (~ as_factor (nom)) Ici, on voit plus clairement que l’étape où il y a le plus de perdition est celle de l’entrée en soins, moins des trois quarts des personnes diagnostiquées étant venu en clinique au mois une fois dans les trois ans suivant le diagnostic. Par contre, l’initiation du traitement une fois entré en clinique et le contrôle de l’infection une fois le traitement initié sont beaucoup plus rapide. Pour aller plus loin avec les outils de l’analyse de survie classique, il serait possible de faire des analyses bivariées (Kaplan-Meier) ou multivariées (Cox) pour chacune de ces étapes. Cependant, il serait plus intéressant de trouver une approache statistique permettant de considérer dans un même modèle l’ensemble des transitions possibles. Une première analyse de séquences sur l’ensemble du fichier L’ analyse de séquences permet d’appréhender l’ensemble de la trajectoire de soins à travers la succession des états dans lesquels se trouvent les patients observés. Nous allons donc réaliser une analyse de séquences (voir le chapitre dédié) sur l’ensemble de notre fichier. Pour cela, il va falloir préalable que nous transformions nos donnée actuellement dans un format long en un tableau large, c’est-à-dire avec une ligne par individu et une variable différentes par pas de temps. On peut réaliser cela facilement avec spread de tidyr (voir le chapitre dédié à tidyr). library (tidyr) large <- care_trajectories %>% dplyr :: select (id, m = month, care_status) %>% spread (key = m, value = care_status, sep = "") head (large) {"columns":[{"label":["id"],"name":[1],"type":["int"],"align":["right"]},{"label":["m0"],"name":[2],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m1"],"name":[3],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m2"],"name":[4],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m3"],"name":[5],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m4"],"name":[6],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m5"],"name":[7],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m6"],"name":[8],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m7"],"name":[9],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m8"],"name":[10],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m9"],"name":[11],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m10"],"name":[12],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m11"],"name":[13],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m12"],"name":[14],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m13"],"name":[15],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m14"],"name":[16],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m15"],"name":[17],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m16"],"name":[18],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m17"],"name":[19],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m18"],"name":[20],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m19"],"name":[21],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m20"],"name":[22],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m21"],"name":[23],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m22"],"name":[24],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m23"],"name":[25],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m24"],"name":[26],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m25"],"name":[27],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m26"],"name":[28],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m27"],"name":[29],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m28"],"name":[30],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m29"],"name":[31],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m30"],"name":[32],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m31"],"name":[33],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m32"],"name":[34],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m33"],"name":[35],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m34"],"name":[36],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m35"],"name":[37],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m36"],"name":[38],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m37"],"name":[39],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m38"],"name":[40],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m39"],"name":[41],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m40"],"name":[42],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m41"],"name":[43],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m42"],"name":[44],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m43"],"name":[45],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m44"],"name":[46],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m45"],"name":[47],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m46"],"name":[48],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m47"],"name":[49],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m48"],"name":[50],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m49"],"name":[51],"type":["S3: haven_labelled"],"align":["right"]},{"label":["m50"],"name":[52],"type":["S3: 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On utilise seqdef de TraMineR pour créer nos séquences, avec les arguments alphabet pour forcer l’ordre de l’alphabet, states pour spécifier des étiquettes courtes à chaque état et cpal pour indiquer le code couleur de chaque état (et être raccord avec nos graphiques précédents). library (TraMineR, quietly = TRUE) TraMineR stable version 2.2-0 (Built: 2020-04-27) Website: http://traminer.unige.ch Please type 'citation("TraMineR")' for citation information. seq_all <- seqdef (large[, m0 : m50], id = large $ id, alphabet = c ("D", "C", "T", "S"), states = c ("diagnostiqué", "en soins", "sous traitement", "inf. contrôlée"), cpal = viridis (4, direction = -1)) [>] found missing values ('NA') in sequence data [>] preparing 2929 sequences [>] coding void elements with '%' and missing values with '*' [>] state coding: [alphabet] [label] [long label] 1 D diagnostiqué diagnostiqué 2 C en soins en soins 3 T sous traitement sous traitement 4 S inf. contrôlée inf. contrôlée [>] 2929 sequences in the data set [>] min/max sequence length: 1/51 On peut retrouver la cascade de soins avec seqdplot . seqdplot (seq_all, legend.prop = .25) Nous allons maintenant calculer une matrice des distances entre individus par optimal matching . Dans le cas présent, nos différents status sont hiérarchiquement ordonnés. Il n’est donc pas raisonnable de penser que les coûts sont constants entre les différents statuts, puisqu’en un sens, passer directement de D à T peut être considéré comme être passé d’abord de D à C puis de C à D . Nous allons donc faire une matrice de coûts hiérarchisée. seqcost nous permets de produire une matrice de coûts constants, que nous allons ensuite modifier manuellement. Pour le coût indel, le plus simple est de considérer la moitié du coût de substitution maximum. couts <- seqcost (seq_all, method = "CONSTANT") [>] creating 4x4 substitution-cost matrix using 2 as constant value couts $indel
[1] 1

$sm
diagnostiqué-> en soins->
diagnostiqué-> 0 2
en soins-> 2 0
sous traitement-> 2 2
inf. contrôlée-> 2 2
sous traitement-> inf. contrôlée->
diagnostiqué-> 2 2
en soins-> 2 2
sous traitement-> 0 2
inf. contrôlée-> 2 0 couts $ sm[1, ] <- c (0, 1, 2, 3) couts $ sm[2, ] <- c (1, 0, 1, 2) couts $ sm[3, ] <- c (2, 1, 0, 1) couts $ sm[4, ] <- c (3, 2, 1, 0) couts $ indel <- max (couts $ sm) / 2 couts $indel
[1] 1.5

$sm
diagnostiqué-> en soins->
diagnostiqué-> 0 1
en soins-> 1 0
sous traitement-> 2 1
inf. contrôlée-> 3 2
sous traitement-> inf. contrôlée->
diagnostiqué-> 2 3
en soins-> 1 2
sous traitement-> 0 1
inf. contrôlée-> 1 0 dist_all <- seqdist (seq_all, method = "OM", sm = couts $ sm, indel = couts $ indel) [>] 2929 sequences with 4 distinct states [>] checking 'sm' (size and triangle inequality) [>] 1370 distinct sequences [>] min/max sequence lengths: 1/51 [>] computing distances using the OM metric [>] elapsed time: 2.77 secs Calculons le dendrogramme et représentons le avec le tapis de séquence grace à seq_heatmap de l’extension JLutils . Pour rappel, cette extension est seulement disponible sur GitHUb . On l’installera donc (ou on la mettra à jour) avec la commande devtools::install_github("larmarange/JLutils") . arbre_all <- hclust (as.dist (dist_all), method = "ward.D2") library (JLutils, quietly = TRUE) ---------------------------------------------------------- You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr) ---------------------------------------------------------- Attaching package: 'plyr' The following objects are masked from 'package:dplyr':

arrange, count, desc, failwith, id, mutate,
rename, summarise, summarize The following object is masked from 'package:purrr':

compact Attaching package: 'magrittr' The following object is masked from 'package:purrr':

set_names The following object is masked from 'package:tidyr':

extract seq_heatmap (seq_all, arbre_all) Il apparaît que les différentes séquences sont principalement regroupées en fonction de leur longueur. En effet, pour passer d’une séquence courte à une séquence longue il faut ajouter des statuts pour compléter la séquence ce qui induit de facto une distance élevée (en raison du coût indel). Dès lors, lorsque l’on travaille avec des séquences aux longueurs très disparates, une classification ascendante hiérarchique va produire une typologie de séquences courtes et de séquences longues, ce qui n’est pas forcément ce que l’on recherche. Dans notre exemple, nous pouvons considérer que les séquences courtes ne sont pas pertinentes à retenir dans l’analyse car l’observation n’est pas assez longue pour voir le parcours de soins des patients. Une solution consiste à ne retenir que les individus observées au moins n mois et analyser leur trajectoire sur seulement n mois, ce qui permet de n’avoir que des séquences de même longueur. Dès lors, la distance entre deux séquences ne dépendra plus que des différences de parcours. On serait tenté de prendre un n élévé pour avoir ainsi des parcours de soins longs. Mais dans ce cas là, l’analyse ne se fera que sur un tout petit nombre d’individus et on manquera de puissance. Si, à l’inverse, on prends un n petit, nous aurons des effectifs élevés mais les séquences seront peut-être trop courtes pour mettre en évidence la variété des trajectoires. Il faut dès lors trouver un compromis entre ces deux contraintes. Si l’on regarde notre premier graphique montrant le nombre d’observations au cours du temps, il apparaît une sorte de point d’inflexion au niveau de M18 avec un brusque décrochage. D’un autre côté, 18 mois offre un minimum de durée d’observations pour espérer voir émerger des trajectoires plus complexes. Une seconde analyse de séquences limitées aux 18 premiers mois Reprenons notre analyse en se limitant aux individus observés au moins 18 mois (soit 19 status entre M0 et M18) et en se limitant aux 18 premiers mois pour modéliser les séquences. La fonction seqlength permets de récupérer la longueur de chaque séquence. large $ seq_length <- seqlength (seq_all) large_m18 <- large[seq_length >= 19, id : m18] seq_m18 <- seqdef (large_m18[, m0 : m18], id = large_m18 $ id, alphabet = c ("D", "C", "T", "S"), states = c ("diagnostiqué", "en soins", "sous traitement", "inf. contrôlée"), cpal = viridis (4, direction = -1)) [>] state coding: [alphabet] [label] [long label] 1 D diagnostiqué diagnostiqué 2 C en soins en soins 3 T sous traitement sous traitement 4 S inf. contrôlée inf. contrôlée [>] 1317 sequences in the data set [>] min/max sequence length: 19/19 dist_m18 <- seqdist (seq_m18, method = "OM", sm = couts $ sm, indel = couts $ indel) [>] 1317 sequences with 4 distinct states [>] checking 'sm' (size and triangle inequality) [>] 578 distinct sequences [>] min/max sequence lengths: 19/19 [>] computing distances using the OM metric [>] elapsed time: 0.42 secs arbre_m18 <- hclust (as.dist (dist_m18), method = "ward.D2") seq_heatmap (seq_m18, arbre_m18) Reste maintenant à décider du nombre de classes à retenir. Encore une fois, c’est un équilibre à trouver entre le niveau de détails voulus et le niveau de simplification requis pour permettre l’analyse. Pour faciliter ce choix, on peut avoir recours à la fonction as.seqtree de l’extension WeightedCluster, couplée à la fonction seqtreedisplay . ATTENTION : pour que le graphique puisse être produit, il faut que le logiciel libre GraphViz (https://graphviz.gitlab.io/) soit installé sur votre PC. On peut également installer GraphViz avec le code ci-dessous : if (! requireNamespace ("BiocManager", quietly = TRUE)) install.packages ("BiocManager") BiocManager :: install ("Rgraphviz") La combinaison des deux fonctions va permettre de représenter l’évolution des catégories au fur-et-à-mesure que l’on coupe le dendrogramme plus bas. On peut choisir le type de graphique utilisé avec l’argument type (voir l’aide de seqplot) et le nombre maximum de clusters avec nclust . library (WeightedCluster, quietly = TRUE) seqtree_m18 <- as.seqtree (arbre_m18, seqdata = seq_m18, diss = dist_m18, ncluster = 7) seqtreedisplay (seqtree_m18, type= "I", border= NA, show.depth= TRUE) Représentation graphique produite par seqtreedisplay Afin d’éviter de multiplier les sous-groupes, nous n’allons conserver que 4 catégories. large_m18 $ typo_cah <- cutree (arbre_m18, 4) Il est aussi possible de se baser sur divers indicateurs statistiques sur la qualité de chaque partition. Pour cela, on pourra par exemple avoir recours à la fonction as.clustrange de l’extension WeightedCluster . Essayez par exemple les commandes ci-après : nc <- as.clustrange (arbre_m18, dist_m18) summary (nc, max.rank = 3) plot (nc, norm = "zscore") Pour plus d’informations, voir le manuel de la librairie WeightedCluster, chapitre 7. On peut représenter le découpage du dendrogramme avec A2Rplot fournie par JLutils A2Rplot (arbre_m18, k = 4, show.labels = FALSE) Comme expliqué par Matthias Studer dans le manuel de la librairie WeightedCluster, plusieurs critiques peuvent être adressées aux procédures hiérarchiques, en particulier le fait que la fusion de deux groupes se fait en maximisant un critère local. L’algorithme PAM pour Partitioning Around Medoids suit une autre logique que les algorithmes hiérarchiques et vise à obtenir la meilleure partition d’un ensemble de données en un nombre prédéfini de groupes. Il a l’avantage de maximiser un critère global et non uniquement un critère local. Par contre, le nombre de classes doit être fixé à l’avance. Ayant décidé de retenir 4 classes au regard de notre classification ascendante hiérarchique, nous pouvons voir si l’algorithme PAM permets d’améliorer nos 4 classes. Nous allons utiliser la fonction wcKMedoids de l’extension WeightedCluster en lui indiquant comme partition initiale celle obtenue avec la classigication hiérarchique. pam_m18 <- wcKMedoids (dist_m18, k = 4, initialclust = arbre_m18) large_m18 $ typo_pam <- pam_m18 $ clustering Un tableau croisé nous permets de voir que les deux typologies restent proches. table (large_m18 $ typo_cah, large_m18 $ typo_pam) 6 23 85 410
1 522 4 0 39
2 0 3 330 3
3 1 203 59 13
4 23 0 5 112 Regardons les tapis de séquence des deux typologies. large_m18 $ ordre_cmd <- cmdscale (as.dist (dist_m18), k = 1) seqIplot (seq_m18, group = large_m18 $ typo_cah, sortv = large_m18 $ ordre_cmd) seqIplot (seq_m18, group = large_m18 $ typo_pam, sortv = large_m18 $ ordre_cmd) Comme on le voit les deux typologies obtenues sont très proches. Suivant le cas, à vous de choisir celle qui semble la plus pertinente d’un point de vue sociologique. Il existe également divers indicateurs statisques pour mesurer la qualité d’une partition (voir le manuel de la librairie WeightedCluster de Matthias Studer). Ils peuvent être calculés avec la fonction wcClusterQuality . Comparons les deux typologies obtenues. tab <- tibble (stat = names (wcClusterQuality (dist_m18, large_m18 $ typo_cah) $ stats), cah = wcClusterQuality (dist_m18, large_m18 $ typo_cah) $ stats, pam = wcClusterQuality (dist_m18, large_m18 $ typo_pam) $ stats) knitr :: kable (tab, digits = 3) stat cah pam PBC 0.703 0.712 HG 0.933 0.950 HGSD 0.930 0.946 ASW 0.528 0.568 ASWw 0.530 0.569 CH 938.972 1023.120 R2 0.682 0.700 CHsq 3351.212 3980.601 R2sq 0.884 0.901 HC 0.028 0.021 Selon ces indicateurs calculés, l’approche PAM obtiendrait une partition légèrement de meilleure qualité que celle obtenuepar CAH. L’extension WeightedCluster fournie aussi une fonction wcSilhouetteObs permettant de mesurer la silhouette de chaque séquence. Plus cette métrique est élevée et proche de 1, plus la séquence est proche du centre de classe et caractéristique de la classe. On peut utiliser cette métrique pour classer les séquences sur le tapis de séquences. large_m18 $ sil <- wcSilhouetteObs (dist_m18, large_m18 $ typo_pam) seqIplot (seq_m18, group = large_m18 $ typo_pam, sortv = large_m18 $ sil) Nous voyons émerger quatre groupes distincts : les rapides, qui entrent en soins et initient le traitement dès les premiers mois suivant le diagnostic ; les lents, qui entrent en soins et initient le traitement plus tardivement, après M6 ; les inaboutis, qui entrent en soins mais n’initient pas le traitement ; les hors soins, qui ne sont pas entrés en soins où n’y sont pas restés. Le graphique obtenu avec seqIplot affiche visuellement chaque groupe avec la même hauteur, pouvant laisser accroire que chaque groupe a le même poids dans l’échantillon. Pour produire une représentation graphique des tapis de séquences plus correcte, où chaque la hauteur de chaque groupe correspondrait à son poids dans l’échantillon, nous allons passer par ggplot2 . Un tapis de séquences peut-être vu comme un raster et dès lors représenté avec geom_raster . Pour travailler avec ggplot2, nos données doivent être au format tidy, c’est-à-dire avec une ligne par point d’observation, soit une ligne par personne et par jour. Nous allons donc repartir du fichier care_trajectories . Le mois d’observation indiquera la position en abscisse. Quant à la position en ordonnée, il faudra que nous la calculions, séparément pour chaque groupe, afin d’éviter des lignes vides dans le graphique. # nommer les groupes large_m18 $ groupe <- factor (large_m18 $ typo_pam, c (85, 23, 410, 6), c ("Rapides", "Lents", "Inaboutis", "Hors soins")) # calculer le rang des individus dans chaque groupe setorder (large_m18, "ordre_cmd") large_m18[, rang_cmd : = 1 : .N, by = groupe] # créer un fichier long long_m18 <- care_trajectories[id %in% large_m18 $ id & month <= 18] long_m18 <- merge (long_m18, large_m18[, .(id, groupe, rang_cmd)], by = "id", all.x = TRUE) long_m18 $ care_statusF <- to_factor (long_m18 $ care_status) # calculer les effectifs par groupe tmp <- large_m18[, .(n = .N), by = groupe] tmp[, groupe_n : = paste0 (groupe, " \n (n=", n, ")")] long_m18 <- merge (long_m18, tmp[, .(groupe, groupe_n)], by = "groupe", all.x = TRUE) # graphique des tapis de séquences ggplot (long_m18) + aes (x = month, y = rang_cmd, fill = care_statusF) + geom_raster () + facet_grid (groupe_n ~ ., space = "free", scales = "free") + scale_x_continuous (breaks = 0 : 6 * 3, labels = paste0 ("M", 0 : 6 * 3)) + scale_y_continuous (breaks = 0 : 5 * 100, minor_breaks = NULL) + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) Facteurs associés à l’appartenance à chaque groupe Une fois les différents groupes de trajectoires identifiés, il est courant de vouloir regarder si certains facteurs influencent l’appartenance à un groupe plutôt qu’un autre. Dans nos données d’exemple, nous nous intéresserons aux variables suivantes : sexe, groupe d’âges et niveau d’éducation. Ces différentes variables sont renseignées pour chaque mois dans le tableau de données care_trajectories, en tenant compte des éventuels changements au cours du temps. Ici, notre analyse est menée au niveau individuel. Nous allons donc récupérer la valeur de ces différentes variables au moment du diagnostic, à savoir à M0. large_m18 <- merge (large_m18, care_trajectories[month == 0, .(id, sex, age, education)], by = "id", all.x = TRUE) L’extension finalfit, présentée dans le chapite sur la régression logistique, permets via sa fonction summary_factorlist de produire rapidement le tableau de l’analyse bivariée. En l’occurence, le code ci-dessous permets de sortir les effectifs, les pourcentages en colonnes et les p-values correspondant au test du Chi² . 1 library (finalfit, quietly = TRUE) Attaching package: 'finalfit' The following object is masked from 'package:labelled':

remove_labels explanatory <- c ("sex", "age", "education") dependent <- "groupe" tab <- summary_factorlist (to_factor (large_m18), dependent, explanatory, p= TRUE, column = TRUE, total_col = TRUE) knitr :: kable (tab, row.names = FALSE) label levels Rapides Lents Inaboutis Hors soins Total p Sexe homme 104 (26.4) 61 (29.0) 42 (25.1) 243 (44.5) 450 (34.2) <0.001 femme 290 (73.6) 149 (71.0) 125 (74.9) 303 (55.5) 867 (65.8) Âge 16-29 96 (24.4) 71 (33.8) 62 (37.1) 235 (43.0) 464 (35.2) <0.001 30-59 269 (68.3) 128 (61.0) 91 (54.5) 281 (51.5) 769 (58.4) 60+ 29 (7.4) 11 (5.2) 14 (8.4) 30 (5.5) 84 (6.4) Education primaire 63 (16.0) 47 (22.4) 30 (18.0) 123 (22.5) 263 (20.0) 0.002 secondaire 126 (32.0) 83 (39.5) 61 (36.5) 212 (38.8) 482 (36.6) supérieur 205 (52.0) 80 (38.1) 76 (45.5) 211 (38.6) 572 (43.4) Une manière de présenter ces mêmes données de manière plus visuelle consiste à réaliser un diagramme en barres cumulées (voir le chapitre sur les graphiques bivariés). Ici, nous utilisons une boucle for (voir le chapitre sur les structures conditionnelles) pour calculer les différents tableaux croisés et les fusionner avec bind_rows (voir la section concaténation de tables du chapitre dédié à dplyr). Nous en profitions également pour calculer le test du Chi² et l’afficher avec le nom de la variable sur le graphique. Note : pour afficher les proportions sur le graphique, aurons recours recours à la statistique stat_prop de l’extension JLutils 2 . res <- tibble () explanatory <- c ("sex" = "Sexe", "age" = "Âge", "education" = "Education") for (v in names (explanatory)) {tmp <- tibble :: as_tibble (table (large_m18 $ groupe, to_factor (large_m18[[v]])), .name_repair = "unique") names (tmp) <- c ("groupe", "level", "n") test <- chisq.test (large_m18 $ groupe, to_factor (large_m18[[v]])) tmp $ var <- paste0 (explanatory[v], " \n ", scales :: pvalue (test $ p.value, add_p = TRUE)) res <- bind_rows (res, tmp)} # stat_prop() a besoin d'un facteur res $ level <- factor (res $ level) library (JLutils) ggplot (res) + aes (x = level, fill = groupe, weight = n) + geom_bar (position = "fill") + geom_text (aes (by = level, label = scales :: percent (..prop.., accuracy = 1)), stat = "prop", position = position_fill (. 5)) + facet_grid (var ~ ., scales = "free", space = "free") + scale_y_continuous (labels = scales :: percent, breaks = 0 : 5 / 5) + coord_flip () + theme (legend.position = "bottom") + xlab ("") + ylab ("") + labs (fill = "") Pour mieux visualiser les relations entre les variables, on peut avoir recours à la fonction ggcross de l’extension JLutils qui permets de représenter les résidus du Chi² . library (JLutils) ggcross (sex + age + education ~ groupe, data = to_factor (large_m18), labels = "percent0(col.prop)", fill_breaks = c (- Inf, -4, -2, 0, 2, 4, Inf), fill_title = "Résidus de Pearson", size = NULL) + theme_minimal () On peut ainsi noter que les hommes, les jeunes et ceux vivant à plus de 10 kilomètres d’une clinique sont plus souvent dans le groupe Hors soins . Inversement, les femmes et les plus éduqués sont plus souvent dans le groupe des Rapides . Résultat inattendu, les ménages les plus riches sont moins souvent dans les groupes ayant initiés un traitement (Rapides et Lents), ce résultat pouvant s’expliquer en partie par le fait que les plus aisés peuvent plus facilement accéder à des soins dans le secteur privé, et donc faussement apparaître Hors soins, car seuls les soins reçus dans le secteur public ont été mesurés dans cette étude. Pour affiner les résultats, on aura recours à un modèle multivarié en exécutant une régression logistique multinomiale avec multinom de l’extension nnet (pour plus de détails, voir le chapitre dédié). library (nnet) large_m18 $ groupe2 <- relevel (large_m18 $ groupe, "Hors soins") regm <- multinom (groupe2 ~ sex + age + education, data = to_factor (large_m18)) tmp <- JLutils :: tidy_detailed (regm, exponentiate = T, conf.int = TRUE) tmp <- tmp[tmp $ term!= "(Intercept)", ] ggplot (tmp) + aes (x = label, y = estimate, ymin = conf.low, ymax = conf.high, color = y.level) + geom_hline (yintercept = 1, color = "gray25", linetype = "dotted") + geom_errorbar (position = position_dodge (0.5), width = 0) + geom_point (position = position_dodge (width = 0.5)) + scale_y_log10 () + coord_flip () + xlab ("Facteurs") + ylab ("Odds Ratios") + labs (color = "vs. Hors Soins") + theme_light () + theme (panel.grid.major.y = element_blank ()) Nous pouvons représenter les effets des variables du modèle avec la fonction ggeffect de ggeffects . library (ggeffects) cowplot :: plot_grid (plotlist = plot (ggeffect (regm)), ncol = 3) Modèle mixte à classes latentes Un autre type d’approche envisageable pour identifier des classes de trajectoires est celle des modèles mixtes à classes latentes . Ce type de modèles peut prendre en compte une grande variété d’indicateurs, continus, binaires ou ordinaux. On peut y intégrer des co-variables et il n’est pas nécessaire de disposer du même nombre d’observations par individu. Nous n’aborderons que brièvement ici ce type de modèles complexes. Sous R, ils peuvent être réalisés via l’extension lcmm et sa fonction homonyme lcmm . Commençons par préparer les données. care_trajectories $ num_status <- as.integer (to_factor (care_trajectories $ care_status)) care_trajectories[, sexF : = to_factor (sex)] care_trajectories[, ageF : = to_factor (age)] care_trajectories[, educationF : = to_factor (education)] Ici, nous allons modéliser le statut dans les soins en fonction du temps. Il faut indiquer au modèle, via le paramètre ng le nombre de groupes ou classes latentes souhaité. Ici, nous avons retenu 4 en lien avec les résultats de notre analyse de séquences. L’argument link = "thresholds" permets d’indiquer que notre variable d’intérêt est ordinale. Les modèles lcmm peuvent également prendre en compte des variables continues. Attention : le temps de calcul de ce type de modèle peut être long (plusieurs heures dans notre exemple), suivant le nombre de paramètres, le nombre d’observations et la puissance de votre machine. library (lcmm) mod4 <- lcmm (num_status ~ month, random = ~ month, subject ='id', mixture = ~ month, ng = 4, idiag = TRUE, data = care_trajectories, link = "thresholds") Voyons comment se présentent les résultats. summary (mod4) General latent class mixed model
fitted by maximum likelihood method

lcmm(fixed = num_status ~ month, mixture = ~month, random = ~month,
subject = "id", ng = 4, idiag = TRUE, link = "thresholds",
data = care_trajectories)

Statistical Model:
Dataset: care_trajectories
Number of subjects: 2929
Number of observations: 49365
Number of latent classes: 4
Number of parameters: 15
Link function: thresholds

Iteration process:
Convergence criteria satisfied
Number of iterations: 52
Convergence criteria: parameters= 5.2e-10
: likelihood= 5.4e-07
: second derivatives= 3.5e-07

Goodness-of-fit statistics:
maximum log-likelihood: -26612.74
AIC: 53255.48
BIC: 53345.22

Discrete posterior log-likelihood: -26612.74
Discrete AIC: 53255.48

Mean discrete AIC per subject: 9.0911
Mean UACV per subject: 9.1045
Mean discrete LL per subject: -9.0859

Maximum Likelihood Estimates:

Fixed effects in the class-membership model:
(the class of reference is the last class)

coef Se Wald p-value
intercept class1 0.00832 0.04503 0.185 0.85334
intercept class2 -0.42002 0.12990 -3.233 0.00122
intercept class3 -0.02992 0.09675 -0.309 0.75712

Fixed effects in the longitudinal model:

coef Se Wald
intercept class1 (not estimated) 0
intercept class2 -1.49825 0.08163 -18.355
intercept class3 -0.41418 0.05228 -7.923
intercept class4 -2.50344 0.09211 -27.179
month class1 0.21757 0.00332 65.447
month class2 0.01032 0.00506 2.039
month class3 0.17720 0.00338 52.373
month class4 0.00449 0.00512 0.876
p-value
intercept class1 (not estimated)
intercept class2 0.00000
intercept class3 0.00000
intercept class4 0.00000
month class1 0.00000
month class2 0.04142
month class3 0.00000
month class4 0.38090

Variance-covariance matrix of the random-effects:
intercept month
intercept 4.39542
month 0.00000 0.12653

Residual standard error (not estimated) = 1

Parameters of the link function:

coef Se Wald p-value
thresh. parm1 0.94803 0.04753 19.945 0.00000
thresh. parm2 1.10727 0.00652 169.922 0.00000
thresh. parm3 1.08980 0.00727 149.816 0.00000 On dispose d’un AIC et d’un BIC. Ainsi, une stratégie possible pour déterminer le nombre de classes consiste à calculer un modèle différent pour chaque nombre de classes envisagé puis à retenir le modèle ayant le plus faible AIC ou BIC. Pour chaque observation, le modèle a calculé la probabilité qu’elle appartienne à chacune des 4 classes identifiées. La fonction postprob fournit des statistiques sur cette classification. postprob (mod4) Posterior classification:
class1 class2 class3 class4
N 844.00 153.00 424.00 1508.00
% 28.82 5.22 14.48 51.49

Posterior classification table:
--> mean of posterior probabilities in each class
prob1 prob2 prob3 prob4
class1 0.5945 0.0735 0.2476 0.0844
class2 0.0924 0.7257 0.0881 0.0938
class3 0.1576 0.1103 0.6561 0.0760
class4 0.1522 0.2052 0.1865 0.4561

Posterior probabilities above a threshold (%):
class1 class2 class3 class4
prob>0.7 23.58 54.90 38.68 6.90
prob>0.8 15.88 48.37 30.19 4.97
prob>0.9 11.26 33.99 21.93 3.51 Les classes et les probabilités d’appartenance à chacune sont disponibles aisément. head (mod4 $ pprob) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["id"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["class"],"name":[2],"type":["int"],"align":["right"]},{"label":["prob1"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["prob2"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["prob3"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["prob4"],"name":[6],"type":["dbl"],"align":["right"]}],"data":[{"1":"3","2":"4","3":"0.1686629","4":"2.218569e-01","5":"2.016242e-01","6":"0.4078560139","_rn_":"1"},{"1":"9","2":"4","3":"0.2123759","4":"2.051987e-01","5":"2.329009e-01","6":"0.3495245093","_rn_":"2"},{"1":"13","2":"4","3":"0.1590213","4":"2.260205e-01","5":"1.879610e-01","6":"0.4269971785","_rn_":"3"},{"1":"15","2":"1","3":"0.9992106","4":"3.337167e-08","5":"3.725161e-07","6":"0.0007889869","_rn_":"4"},{"1":"18","2":"1","3":"0.5616344","4":"4.729766e-02","5":"3.478822e-01","6":"0.0431858170","_rn_":"5"},{"1":"21","2":"4","3":"0.1686629","4":"2.218569e-01","5":"2.016242e-01","6":"0.4078560139","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Récupérons la classe dans notre fichier de données. care_trajectories <- merge (care_trajectories, mod4 $ pprob %>% dplyr :: select (id, mod4_class = class), by = "id", all.x = TRUE) Améliorons les intitulés des classes et ajoutons le nombre d’individu par classes. n_par_classe <- table (mod4 $ pprob $ class) n_par_classe 1 2 3 4
844 153 424 1508 care_trajectories $ mod4_class2 <- factor (care_trajectories $ mod4_class, levels = 1 : 4, labels = paste0 ("Classe ", 1 : 4, " (n=", n_par_classe, ")")) Représentons la cascade observée dans chacune de ces classes. care_trajectories $ care_statusF <- to_factor (care_trajectories $ care_status) ggplot (care_trajectories[month <= 36]) + aes (x = month, fill = care_statusF) + geom_bar (color = "gray50", width = 1, position = "fill") + scale_x_continuous (breaks = 0 : 6 * 6, labels = paste0 ("M", 0 : 6 * 6)) + scale_y_continuous (labels = scales :: percent) + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) + facet_grid (~ mod4_class2) Une manière alternative de présenter les classes consiste à représenter chaque mois, non pas la distribution dans chaque état, mais un état moyen en considérant que le statut dans les soins peut être assimilé à un score allant de 1 à 4. moyennes_mensuelles <- care_trajectories[month <= 36, .(status_moyen = mean (num_status)), by = .(month, mod4_class2)] ggplot (moyennes_mensuelles) + aes (x = month, y = status_moyen, color = mod4_class2) + geom_line (size = 2) + scale_x_continuous (breaks = 0 : 6 * 6, labels = paste0 ("M", 0 : 6 * 6)) + scale_y_continuous (breaks = 1 : 4, limits = c (1, 4), labels = c ("diagnostiqué", "suivi", "sous traitement", "contrôlé")) + xlab ("") + ylab ("Statut moyen") + labs (color = "") + theme_classic () + theme (legend.position = "bottom", panel.grid.major = element_line (colour = "grey80")) Il faut cependant rester vigilant, dans la mesure où ce type de représentation synthétique peut masquer la diversité des trajectoires sous-jacentes, notamment en termes de durée ou d’enchaînement des événements. Même si cela est peut-être plus difficile à lire, il est toujours bon de regarder les tapis de séquences. care_trajectories[, tmp_rang : = as.integer (fct_infreq (factor (id))), by = mod4_class] ggplot (care_trajectories[month <= 36]) + aes (x = month, y = tmp_rang, fill = care_statusF) + geom_raster () + facet_grid (mod4_class2 ~ ., space = "free", scales = "free") + scale_x_continuous (breaks = 0 : 6 * 6, labels = paste0 ("M", 0 : 6 * 6)) + scale_y_continuous (breaks = 0 : 5 * 200, minor_breaks = NULL) + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) Modèle à observations répétées Pour prendre en considération l’ensemble des observations présentes (sans se limiter aux individus observés au moins sur une certaine période), nous pouvons avoir recours à un modèle à observations répétées . Il s’agit de modèles classiques sauf qu’au lieu de considérer une ligne par individu, nous allons intégrer dans le modèle une ligne par individu et par pas de temps. Dans la mesure où nous avons plusieurs observations pour une même personne, cela doit être pris en compte par l’ajout d’un effet aléatoire dans le cadre d’un modèle mixte ou en ayant recours à un (voir le chapitre sur les modèles à effets aléatoires). Vue la nature de notre variable d’intérêt (plusieurs modalités ordonnées), nous aurons recours à une régression logistique ordinale (voir le chapitre dédié). Pour un modèle mixte ordinal on peut utiliser la fonction clmm de l’extension ordinal . Pour un modèle GEE ordinal, citons ordgee de l’extension geepack ou encore ordLORgee de multgee . Il importe également que la dimension temporelle soit inclue dans les variables du modèle. Ici, nous allons utiliser ordgee . Il nous faut tout d’abord transformer notre variable d’intérêt en un facteur ordonné. care_trajectories[, care_statusF : = to_factor (care_status, ordered = TRUE)] Nous allons transformer nos variables explicatives en facteurs. Pour le temps, dans la mesure où sont effet n’est pas forcément linéaire, nous allons l’intégrer en tant que variable catégorielle. Par contre, comme nous n’avons que très peu d’observations individuelles après 3 ans, nous ne prendrons en compte que les observations des 36 premiers mois. Nous allons aussi retirer les observations à M0 puisqu’à ce moment précis tous les individus sont dans la même situation (diagnostiqués mais pas en soins.) ct36 <- care_trajectories[month > 0 & month <= 36, ] ct36[, sexF : = to_factor (sex)] ct36[, ageF : = to_factor (age)] ct36[, educationF : = to_factor (education)] ct36[, monthF : = to_factor (month)] Calculons notre modèle. library (geepack) mod_td <- ordgee (care_statusF ~ sexF + ageF + educationF + monthF, data = ct36, id = ct36 $ id) Les coefficients du modèle s’obtiennent avec summary . Malheureusement, il n’existe pas de tieder pour ce type de modèle. Nous allons donc procéder manuellement. res <- summary (mod_td) $ mean res $ term <- rownames (res) head (res) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["estimate"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["san.se"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["wald"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["p"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["term"],"name":[5],"type":["chr"],"align":["left"]}],"data":[{"1":"-2.8569848","2":"0.12723763","3":"504.17899","4":"0.000000e+00","5":"Inter:diagnostiqué, mais pas suivi","_rn_":"Inter:diagnostiqué, mais pas suivi"},{"1":"-3.4247822","2":"0.12893566","3":"705.53705","4":"0.000000e+00","5":"Inter:suivi, mais pas sous traitement","_rn_":"Inter:suivi, mais pas sous traitement"},{"1":"-3.9464056","2":"0.13203443","3":"893.36522","4":"0.000000e+00","5":"Inter:sous traitement, mais infection non contrôlée","_rn_":"Inter:sous traitement, mais infection non contrôlée"},{"1":"0.7419722","2":"0.08677773","3":"73.10695","4":"0.000000e+00","5":"sexFfemme","_rn_":"sexFfemme"},{"1":"0.5576609","2":"0.08900306","3":"39.25822","4":"3.713028e-10","5":"ageF30-59","_rn_":"ageF30-59"},{"1":"0.7147037","2":"0.19302434","3":"13.70970","4":"2.133496e-04","5":"ageF60+","_rn_":"ageF60+"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Les intervalles de confiance à 95% ne sont pas déjà calculés. Faisons-le donc nous même. mult <- stats :: qnorm ((1 + .95) / 2) res $ conf.low <- res $ estimate - mult * res $ san.se res $ conf.high <- res $ estimate + mult * res $ san.se Enfin, nous souhaitons disposer des odds ratios et non des coefficients bruts. Il faut avoir recours à la fonction exp (exponentielle). res $ estimate <- exp (res $ estimate) res $ conf.low <- exp (res $ conf.low) res $ conf.high <- exp (res $ conf.high) Préparons un tableau avec les résultats. Pour le rendre plus lisible, nous allons mettre en forme les odds ratios avec une seule décimale. Améliorer le rendu des p-values et nous allons utiliser la virgule comme séparateur de décimal, comme il se doit. Nous aurons recours aux fonctions number et pvalue de l’extension scales (voir le chapitre sur la mise en forme des nombres). tab <- res %>% mutate (estimate = scales :: number (estimate, accuracy = .01, decimal.mark = ","), p = scales :: pvalue (p, decimal.mark = ","), conf.low = scales :: number (conf.low, accuracy = .1, decimal.mark = ","), conf.high = scales :: number (conf.high, accuracy = .1, decimal.mark = ",")) %>% dplyr :: select (Facteur = term, OR = estimate, "p-value" = p, "IC 95% bas" = conf.low, "IC 95% haut" = conf.high) knitr :: kable (tab, row.names = FALSE, align = "lrrrr") Facteur OR p-value IC 95% bas IC 95% haut Inter:diagnostiqué, mais pas suivi 0,06 <0,001 0,0 0,1 Inter:suivi, mais pas sous traitement 0,03 <0,001 0,0 0,0 Inter:sous traitement, mais infection non contrôlée 0,02 <0,001 0,0 0,0 sexFfemme 2,10 <0,001 1,8 2,5 ageF30-59 1,75 <0,001 1,5 2,1 ageF60+ 2,04 <0,001 1,4 3,0 educationFsecondaire 1,17 0,109 1,0 1,4 educationFsupérieur 1,40 0,002 1,1 1,7 monthF2 1,06 0,147 1,0 1,1 monthF3 1,36 <0,001 1,2 1,5 monthF4 2,79 <0,001 2,5 3,1 monthF5 3,82 <0,001 3,4 4,3 monthF6 4,69 <0,001 4,1 5,4 monthF7 5,63 <0,001 4,9 6,4 monthF8 6,40 <0,001 5,6 7,3 monthF9 7,02 <0,001 6,1 8,1 monthF10 7,49 <0,001 6,5 8,6 monthF11 7,96 <0,001 6,9 9,2 monthF12 8,42 <0,001 7,3 9,8 monthF13 9,31 <0,001 8,0 10,8 monthF14 10,11 <0,001 8,6 11,8 monthF15 10,74 <0,001 9,2 12,6 monthF16 11,46 <0,001 9,8 13,4 monthF17 11,43 <0,001 9,7 13,4 monthF18 11,53 <0,001 9,8 13,6 monthF19 11,50 <0,001 9,7 13,6 monthF20 11,70 <0,001 9,7 14,1 monthF21 11,67 <0,001 9,6 14,2 monthF22 12,60 <0,001 10,3 15,4 monthF23 12,78 <0,001 10,4 15,7 monthF24 12,76 <0,001 10,4 15,7 monthF25 13,11 <0,001 10,6 16,2 monthF26 13,60 <0,001 11,0 16,9 monthF27 14,40 <0,001 11,6 17,9 monthF28 15,65 <0,001 12,5 19,6 monthF29 14,89 <0,001 11,9 18,7 monthF30 14,98 <0,001 11,9 18,9 monthF31 14,02 <0,001 11,0 17,9 monthF32 14,27 <0,001 11,0 18,5 monthF33 14,41 <0,001 11,0 18,9 monthF34 14,73 <0,001 11,0 19,7 monthF35 16,17 <0,001 11,8 22,1 monthF36 16,07 <0,001 11,4 22,6 On peut facilement représenter tout cela graphiquement. On va supprimer les termes seuils, grâce à str_detect de stringr . On notera le recours à fct_inorder de forcats pour conserver l’ordre des termes selon leur ordre d’apparition. res <- res[! str_detect (res $ term, "Inter"),] res $ term <- fct_inorder (res $ term) res $ term <- fct_recode (res $ term, "femme vs. homme" = "sexFfemme", "âge : 30-59 vs. 16-29" = "ageF30-59", "âge : 60+ vs 16-29" = "ageF60+", "éducation : secondaire vs. primaire" = "educationFsecondaire", "éducation : supérieure vs. primaire" = "educationFsupérieur", "M2" = "monthF2", "M3" = "monthF3", "M4" = "monthF4", "M5" = "monthF5", "M6" = "monthF6", "M7" = "monthF7", "M8" = "monthF8", "M9" = "monthF9", "M10" = "monthF10", "M11" = "monthF11", "M12" = "monthF12", "M13" = "monthF13", "M14" = "monthF14", "M15" = "monthF15", "M16" = "monthF16", "M17" = "monthF17", "M18" = "monthF18", "M19" = "monthF19", "M20" = "monthF20", "M21" = "monthF21", "M22" = "monthF22", "M23" = "monthF23", "M24" = "monthF24", "M25" = "monthF25", "M26" = "monthF26", "M27" = "monthF27", "M28" = "monthF28", "M29" = "monthF29", "M30" = "monthF30", "M31" = "monthF31", "M32" = "monthF32", "M33" = "monthF33", "M34" = "monthF34", "M35" = "monthF35", "M36" = "monthF36") res $ var_group <- c ("a", "b", "b", "c", "c", rep ("d", 35)) GGally :: ggcoef (res, exponentiate = TRUE, mapping = aes (x = estimate, y = fct_rev (term), color = var_group)) + theme_classic () + ylab ("") + xlab ("Odds Ratio") + theme (legend.position = "none") Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2 Sur ce graphique, on visualise bien l’évolution temporelle traduite par les odds ratios associés à chaque mois, ainsi que les effets globaux de nos covariables : les femmes ont une meilleure progression dans la cascade de soins que les hommes, de même que les plus éduqués et les plus âgés. Modèle de survie multi-états Depuis la fin du XX e siècle, de nombreux développements ont réalisés pour étendre les modèles de survie à des processus multi-états . Ces modèles permettent de considérer une grande variété de processus. Plusieurs implémentations existent dans R 3 . Ici, nous allons utiliser l’extension msm qui repose sur des modèles de Markov multi-états et peut prendre en compte des co-variables dans le modèle. En premier lieu, pour cette extension, ils nous faut disposer des données sous une forme longue, c’est-à-dire avec une ligne par individu et point d’observation dans le temps, ce qui est déjà le cas du fichier care_trajectories . Les différents status possibles doivent également être codés sous la forme de nombres entiers croissants (ici 1 correspondra à D, 2 à C, 3 à T et 4 à S). library (msm) care_trajectories $ status <- as.integer (to_factor (care_trajectories $ care_status)) setorder (care_trajectories, id, month) Par ailleurs, nous n’allons concerver dans l’analyse que les individus avec au moins deux points d’observation, ici ceux observés au moins jusqu’à un mois. ct <- care_trajectories[id %in% care_trajectories[month == 1, id]] La fonction statetable.msm permet de calculer le nombre et le type de transitions observées dans les données. statetable.msm (status, id, data = ct) to
from 1 2 3 4
1 21916 1168 467 247
2 501 4323 597 210
3 26 141 3489 770
4 33 230 43 12275 Il faut ensuite définir les transitions possibles dans le modèle en faisant une matrice carrée. On indiquera 0 si la transition n’est pas possible, une valeur positive sinon. tr <- rbind (c (0, 1, 0, 0), # de 1 : vers 2 c (1, 0, 1, 0), # de 2 : vers 1 ou vers 3 c (0, 1, 0, 1), # de 3 : vers 2 ou vers 4 c (0, 0, 1, 0) # de 4 : vers 3) Dans notre matrice de transitions, nous n’avons pas défini de transition directe entre le statut 1 et le statut 3, alors que de telles transitions sont pourtant observées dans notre fichier. En fait, nous pouvons considérer qu’une transition de 1 vers 3 correspond en fait à deux transitions successives, de 1 vers 2 puis de 2 vers 3. La fonction msm s’aura identifier d’elle-mêmes ces doubles, voire triples, transitions. On peut facilement représenter notre matrice de transition sous forme de schéma à l’aide de l’excellente extension DiagrammeR . library (DiagrammeR) mermaid (" graph TD 1[diagnostiqué, mais pas suivi] 2[suivi, mais pas sous traitement] 3[sous traitement, mais infection non contrôlée] 4[sous traitement et infection contrôlée] 1--entrée en soins-->2 2--initiation traitement-->3 2--sortie de soins-->1 3--contrôle infection-->4 3--arrêt traitement-->2 4--échec virologique-->3 ", height = 300) {"x":{"diagram":"\ngraph TD\n1[diagnostiqué, mais pas suivi]\n2[suivi, mais pas sous traitement]\n3[sous traitement, mais infection non contrôlée]\n4[sous traitement et infection contrôlée]\n\n1--entrée en soins-->2\n2--initiation traitement-->3\n2--sortie de soins-->1\n3--contrôle infection-->4\n3--arrêt traitement-->2\n4--échec virologique-->3\n"},"evals":[],"jsHooks":[]} Il ne nous reste plus qu’à spécifier notre modèle. L’option obstype = 1 indique à msm que nos données correspondent à des snapshots à certains moments donnés (ici tous les mois) et donc que les transitions d’un état à un autre ont eu lieu entre nos points d’observation. Les types 2 et 3 correspondent à des dates de transition exactes (voir l’aide la fonction pour plus de détails). ms_mod <- msm (status ~ month, subject = id, data = ct, qmatrix = tr, obstype = 1) En exécutant cette commande, vous risquez d’obtenir le message d’erreur suivant : Error in Ccall.msm(params, do.what = "lik", ...) : numerical overflow in calculating likelihood Cela est dû à un problème d’échelle dans l’optimisation du modèle qui génère des nombres plus grands que ce que peux gérer l’ordinateur. Il peut être résolu de la manière suivante. Tout d’abord, on reexécute le modèle avec l’option control = list(trace = TRUE) . ms_mod <- msm (status ~ month, subject = id, data = ct, qmatrix = tr, obstype = 1, control = list (trace = TRUE)) On obtient le message suivant : initial value 74796.800445
Error in Ccall.msm(params, do.what = "lik", ...) : numerical overflow in calculating likelihood Ce qui importe de retenir, c’est la valeur initiale du paramètre d’optimisation avant que l’erreur ne se produise. On va l’utiliser (ou une valeur proche) comme paramètre d’échelle pour l’optimation avec l’option fnscale : ms_mod <- msm (status ~ month, subject = id, data = ct, qmatrix = tr, obstype = 1, control = list (fnscale = 75000, trace = TRUE)) initial value 0.997291
iter 10 value 0.520302
iter 20 value 0.497598
iter 30 value 0.497487
final value 0.497484
converged
Used 39 function and 37 gradient evaluations On peut comparer la prévalence dans chaque état au cours du temps telle que modélisée par le modèle avec les valeurs observées avec la fonction plot.prevalence.msm . plot.prevalence.msm (ms_mod) Par défaut, msm considère que les intensités de transition d’un état à un autre sont constantes au cours du temps. Or, dans notre example, il apparait que les prévalences observées varient différemment pendant les premiers mois après le diagnostic. Nous allons donc recalculer le modèle en spécifiant avec le paramètre pci que nous souhaitons considérer des intensités de transition différentes pour les trois premiers mois, la première année, la seconde année et après la seconde année. Comme il faudra plus d’itérations pour faire converger notre modèle, nous avons également augmenter la valeur du paramètre maxit (100 par défaut). ms_mod <- msm (status ~ month, subject = id, data = ct, qmatrix = tr, obstype = 1, pci = c (3, 12, 24), control = list (fnscale = 75000, trace = TRUE, maxit = 500)) Comparons à nouveau les prévalences estimées avec les prévalences observées. plot.prevalence.msm (ms_mod) Comme on peut le voir, l’ajustement entre les deux a été amélioré. Les prévalences elles-mêmes peuvent s’obtenir avec prevalence.msm . prevalence.msm (ms_mod) $Observed
State 1 State 2 State 3 State 4 Total
0 2799 24 0 7 2830
5 1551 292 297 460 2600
10 958 257 210 623 2048
15 578 168 104 603 1453
20 283 87 65 327 762
25 192 63 39 267 561
30 147 41 23 245 456
35 69 17 12 136 234
40 14 2 2 27 45
45 7 0 0 12 19
50 0 0 0 1 1

$Expected
State 1 State 2 State 3 State 4 Total
0 2799.0000000 24.00000000 0.00000000 7.0000000 2830
5 1436.9517812 424.94481382 358.54921455 379.5541904 2600
10 1017.4844830 258.18412962 191.08425368 581.2471337 2048
15 667.5131754 168.10101158 98.36234055 519.0234724 1453
20 331.9787922 81.73411456 46.82691929 301.4601739 762
25 233.1379082 55.54030396 32.92027747 239.4015104 561
30 184.4057933 40.32826279 24.41822743 206.8477165 456
35 91.3576268 19.57230370 12.12106094 110.9490085 234
40 16.9423183 3.63904521 2.30424907 22.1143875 45
45 6.9062654 1.49869230 0.96896925 9.6260731 19
50 0.3515898 0.07725341 0.05092087 0.5202359 1

$`Observed percentages`
State 1 State 2 State 3 State 4
0 98.90459 0.8480565 0.000000 0.2473498
5 59.65385 11.2307692 11.423077 17.6923077
10 46.77734 12.5488281 10.253906 30.4199219
15 39.77977 11.5622849 7.157605 41.5003441
20 37.13911 11.4173228 8.530184 42.9133858
25 34.22460 11.2299465 6.951872 47.5935829
30 32.23684 8.9912281 5.043860 53.7280702
35 29.48718 7.2649573 5.128205 58.1196581
40 31.11111 4.4444444 4.444444 60.0000000
45 36.84211 0.0000000 0.000000 63.1578947
50 0.00000 0.0000000 0.000000 100.0000000

$`Expected percentages`
State 1 State 2 State 3 State 4
0 98.90459 0.8480565 0.000000 0.2473498
5 55.26738 16.3440313 13.790354 14.5982381
10 49.68186 12.6066470 9.330286 28.3812077
15 45.94034 11.5692369 6.769604 35.7208171
20 43.56677 10.7262618 6.145265 39.5617026
25 41.55756 9.9002324 5.868142 42.6740660
30 40.43987 8.8439173 5.354874 45.3613413
35 39.04172 8.3642323 5.179941 47.4141062
40 37.64960 8.0867671 5.120553 49.1430833
45 36.34877 7.8878542 5.099838 50.6635426
50 35.15898 7.7253409 5.092087 52.0235901 Ceci dit, le format dans lequel sont renvoyées les prévalences n’est que peu pratique pour les exploiter ensuite, par exemple avec ggplot2 . L’extension JLutils fournit une fonction expérimentale tidy.prevalence.msm 4 permettant de transformer ce résultat dans un format tidy . library (JLutils) prev <- tidy.prevalence.msm (prevalence.msm (ms_mod, times = 0 : 36)) head (prev) {"columns":[{"label":["time"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["status"],"name":[2],"type":["chr"],"align":["left"]},{"label":["observed"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["expected"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["observed.percentage"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["expected.percentage"],"name":[6],"type":["dbl"],"align":["right"]}],"data":[{"1":"0","2":"State 1","3":"2799","4":"2799.000","5":"98.90459","6":"98.90459"},{"1":"1","2":"State 1","3":"1942","4":"2578.661","5":"68.62191","6":"91.11875"},{"1":"2","2":"State 1","3":"1766","4":"2350.207","5":"63.54804","6":"84.57026"},{"1":"3","2":"State 1","3":"1651","4":"2144.540","5":"60.76555","6":"78.93045"},{"1":"4","2":"State 1","3":"1589","4":"1973.099","5":"59.58005","6":"73.98196"},{"1":"5","2":"State 1","3":"1551","4":"1808.973","5":"59.65385","6":"69.57589"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} prev $ status <- to_factor (prev $ status) casc_status <- c ("diagnostiqué, mais pas suivi", "suivi, mais pas sous traitement", "sous traitement, mais infection non contrôlée", "sous traitement et infection contrôlée") levels (prev $ status) <- casc_status Il est alors ensuite facile de produire le graphique de la cascade de soins, estimée par le modèle, que l’on pourra mettre en comparaison de la cascade observée que nous avions calculé tout à l’heure. casc_est <- ggplot (prev) + aes (x = time, fill = status, weight = expected) + geom_bar (color = "gray50", width = 1, position = "fill") + scale_x_continuous (breaks = 0 : 6 * 6, labels = paste0 ("M", 0 : 6 * 6)) + scale_y_continuous (labels = scales :: percent) + ggtitle ("Cascade des soins estimée, selon le temps depuis le diagnostic") + xlab ("") + ylab ("") + theme_light () + theme (legend.position = "bottom") + labs (fill = "Statut dans les soins") + scale_fill_viridis (discrete = TRUE, direction = -1) + guides (fill = guide_legend (nrow = 2)) multiplot (casc_obs, casc_est) Comme on peut le voir, dans le modèle, l’évolution est plus lissée comparativement aux données brutes observées. Un des intérêts de msm est la possibilité d’y intégrer des covariables, permettant ainsi de calculer un modèle multivarié. Les variables peuvent être dépendantes du temps, puisqu’il suffit de renseigner leur valeur à chaque point d’observation. ct $ sex <- to_factor (ct $ sex) ct $ age <- to_factor (ct $ age) ct $ education <- to_factor (ct $ education) ms_mod_mult <- msm (status ~ month, subject = id, data = ct, qmatrix = tr, obstype = 1, pci = c (3, 12, 24), control = list (fnscale = 75000, trace = TRUE, maxit = 500), covariates = ~ sex + age + education) Les risques relatifs, ou hazard ratios en anglais, associés à chaque covariable et à chaque transition s’obtiennent avec hazard.msm . Une fois encore, le format de sortie n’est pas le plus adapté pour un traitement graphique, mais on pourra avoir recours à tidy.hazard.msm de JLutils . hr <- tidy.hazard.msm (hazard.msm (ms_mod_mult)) head (hr) {"columns":[{"label":["term"],"name":[1],"type":["chr"],"align":["left"]},{"label":["transition"],"name":[2],"type":["chr"],"align":["left"]},{"label":["from"],"name":[3],"type":["chr"],"align":["left"]},{"label":["to"],"name":[4],"type":["chr"],"align":["left"]},{"label":["estimate"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["conf.low"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["conf.high"],"name":[7],"type":["dbl"],"align":["right"]}],"data":[{"1":"sexfemme","2":"State 1 - State 2","3":"State 1","4":"State 2","5":"1.8855232","6":"1.7059612","7":"2.083985"},{"1":"sexfemme","2":"State 2 - State 1","3":"State 2","4":"State 1","5":"0.9245660","6":"0.7655997","7":"1.116539"},{"1":"sexfemme","2":"State 2 - State 3","3":"State 2","4":"State 3","5":"0.9015913","6":"0.8044458","7":"1.010468"},{"1":"sexfemme","2":"State 3 - State 2","3":"State 3","4":"State 2","5":"1.0959810","6":"0.8854966","7":"1.356498"},{"1":"sexfemme","2":"State 3 - State 4","3":"State 3","4":"State 4","5":"1.3985443","6":"1.2300980","7":"1.590057"},{"1":"sexfemme","2":"State 4 - State 3","3":"State 4","4":"State 3","5":"0.8610144","6":"0.6678918","7":"1.109979"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} On va recoder certaines étiquettes en vue de faire un graphique des résultats. hr $ type <- "Transition ascendante" hr[hr $ transition == c ("State 2 - State 1"),] $ type <- "Transition descendante" hr[hr $ transition == c ("State 3 - State 2"),] $ type <- "Transition descendante" hr[hr $ transition == c ("State 4 - State 3"),] $ type <- "Transition descendante" hr $ term <- fct_recode (fct_inorder (hr $ term), "femme vs. homme" = "sexfemme", "30-59 vs. 16-29" = "age30-59", "60+ vs. 16-29" = "age60+", "éduc. secondaire vs. primaire" = "educationsecondaire", "éduc. supérieure vs. primaire" = "educationsupérieur") hr $ transition <- fct_recode (fct_inorder (hr $ transition), "entrée en soins" = "State 1 - State 2", "sortie de soins" = "State 2 - State 1", "initiation traitement" = "State 2 - State 3", "arrêt traitement" = "State 3 - State 2", "contrôle infection" = "State 3 - State 4", "échec virologique" = "State 4 - State 3") head (hr) {"columns":[{"label":["term"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["transition"],"name":[2],"type":["fctr"],"align":["left"]},{"label":["from"],"name":[3],"type":["chr"],"align":["left"]},{"label":["to"],"name":[4],"type":["chr"],"align":["left"]},{"label":["estimate"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["conf.low"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["conf.high"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["type"],"name":[8],"type":["chr"],"align":["left"]}],"data":[{"1":"femme vs. homme","2":"entrée en soins","3":"State 1","4":"State 2","5":"1.8855232","6":"1.7059612","7":"2.083985","8":"Transition ascendante"},{"1":"femme vs. homme","2":"sortie de soins","3":"State 2","4":"State 1","5":"0.9245660","6":"0.7655997","7":"1.116539","8":"Transition descendante"},{"1":"femme vs. homme","2":"initiation traitement","3":"State 2","4":"State 3","5":"0.9015913","6":"0.8044458","7":"1.010468","8":"Transition ascendante"},{"1":"femme vs. homme","2":"arrêt traitement","3":"State 3","4":"State 2","5":"1.0959810","6":"0.8854966","7":"1.356498","8":"Transition descendante"},{"1":"femme vs. homme","2":"contrôle infection","3":"State 3","4":"State 4","5":"1.3985443","6":"1.2300980","7":"1.590057","8":"Transition ascendante"},{"1":"femme vs. homme","2":"échec virologique","3":"State 4","4":"State 3","5":"0.8610144","6":"0.6678918","7":"1.109979","8":"Transition descendante"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Vu le nombre de coefficients (un risque relatif par covariable et par transition), on va organiser le graphique en distinguant les transitions acsendantes et les transitions descendantes, d’une part, et en regroupant les risques relatifs d’une même covariable, d’autre part. Pour alléger le graphique, nous allons également retirer la covariable timeperiod créé par l’argument pci, en ayant recours à str_detect de l’extension stringr pour repérer les lignes en questions (voir le chapitre sur la manipulation de texte). ggplot (data = hr[! str_detect (hr $ term, "time"), ]) + aes (x = fct_rev (term), y = estimate, color = fct_rev (transition), ymin = conf.low, ymax = conf.high) + geom_hline (yintercept = 1, color = "gray25", linetype = "dotted") + geom_errorbar (position = position_dodge (0.5), width = 0) + geom_point (position = position_dodge (0.5)) + scale_y_log10 () + facet_grid (~ type) + coord_flip () + theme_classic () + theme (legend.position = "bottom") + ylab ("risque relatif") + xlab ("") + labs (color = "Transition") + scale_color_brewer (palette = "Paired") + guides (color = guide_legend (reverse = TRUE)) Ce modèle de survie multi-états permet de mettre en évidence des effets différenciés selon la transition considérée. Par exemple, les femmes sont plus rapides en matière d’entrée en soins et de contrôle de l’infection (une fois le traitement initié) mais plus lentes à démarrer le traitement (une fois entrées en soins). Par contre, le sexe ne semble pas jouer sur les transitions descendantes (échec virologique, arrête du traitement ou sortie de soins). Pour plus de détails sur les tableaux croisés et le test du Chi², voir le chapitre sur la statisque bivariée et le chapitre sur les tests de comparaison . Cette extension n’est disponible que sur GitHub . On peut l’installer avec la commande suivante : source("https://install-github.me/larmarange/JLutils") Comme par exemple msSurv pour une estimation non-paramétrique. avant l’éventuelle intégration diretement dans msm d’un tidier officiel. Analyse de réseaux Un bon tutoriel pour s’initier à la visualisation des réseaux avec R, Network visualization with R de Katherine Ognyanova, est disponible en ligne : http://kateto.net/network-visualization . On pourra poursuivre avec différents billets de blog publiés par François Briatte sur https://politbistro.hypotheses.org/ . Enfin, François Briatte a également recensé de nombreuses ressources en ligne sur l’analyse de réseau dans son An awesome list of network analysis resources (http://f.briatte.org/r/awesome-network-analysis-list). Analyse spatiale Il est tout à fait possible de réaliser des analyses spatiales sous R . Historiquement, l’extension principale pour la gestion des objets spatiaux sous R est l’extension sp . Depuis quelques années, une nouvelle extension sf s’est développée. Alors, faut-il plutôt apprendre sp ou sf ? Chris Brown tente de répondre à cette question dans son billet Should I learn sf or sp for spatial R programming? . Ces deux extensions ont leurs avantages et inconvénients. Du fait que sp est plus ancienne, elle est compatible avec plus d’autres extensions. De l’autre côté, sf peut s’avérer plus simple pour le néophyte. Dans tous les cas, l’extension raster sera un bon complément pour gérer les données de type raster . Pour une présentation détaillée (en anglais) de l’analyse spatiale sous R, on pourra se référer à l’ouvrage Geocomputation with R de Robin Lovelace, Jakub Nowosad et Jannes Muenchow, consultable en ligne (https://geocompr.robinlovelace.net/). Cette ouvrage privilégie plutôt l’extension sf . On pourra également se référer aux différentes vignettes inclues par leurs extensions et consultables en ligne sur : https://cran.r-project.org/package=sp pour sp https://cran.r-project.org/package=sf pour sf https://cran.r-project.org/package=raster pour raster Pour la réalisation de cartes avec R, on pourra se référer au chapitre dédié . Enfin, le site Awesome R fournit une sélection d’extensions dédiées à l’analyse spatiale. ggplot2 et la grammaire des graphiques Ouvrages Assistants pour ggplot2 Ce chapitre est en cours d’écriture. On pourra se référer au chapitre 5 Graphiques du support de cours d’Ewen Gallic intitulé Logiciel R et programmation (http://egallic.fr/Enseignement/R/m1_stat_eco_logiciel_R.pdf), en complément des deux chapitres introductifs d’analyse-R : introduction à ggplot2 et graphiques bivariés avec ggplot2 . Grammaire des graphiques de ggplot2 Ouvrages ggplot2: Elegant Graphics for Data Analysis (Use R!) d’Hadley Wickham 1 R Graphics Cookbook de Winston Chang Assistants pour ggplot2 Plusieurs extensions propose une assistance visuelle pour l’utilisation de ggplot2 via des add-ins dans RStudio : esquisse pour explorer visuellement des données et produire des graphiques de base avec ggplot2 (plus d’informations sur https://github.com/dreamRs/esquisse) ; ggplotAssist pour générer des graphiques ggplot2 étape par étape (plus d’informations sur https://github.com/cardiomoon/ggplotAssist/) ; ggThemeAssist pour éditer le thème d’un graphique déjà réalisé. Une version PDF est disponible sur http://moderngraphics11.pbworks.com/f/ggplot2-Book09hWickham.pdf . Étendre ggplot2 Nouvelles géométries Étiquettes non superposées Graphiques en sucettes (lollipop) Graphique d’haltères (dumbbell) Accolades de comparaison (bracket) Diagramme en crêtes (ridges) Graphique en gaufres (waffle) Graphique en mosaïque (mosaic plot) Graphique de pirates : alternative aux boîtes à moustache (pirat plot) Axes, légende et facettes Axes limités Répéter les étiquettes des axes sur des facettes Cartes Graphiques complexes Graphiques divergents Graphiques interactifs Graphiques animés Surligner certaines données Thèmes et couleurs Palettes de couleurs hrbrthemes ggthemes Combiner plusieurs graphiques De nombreuses extensions permettent d’étendre les possibilités graphiques de ggplot2 . Certaines ont déjà été abordées dans les différents chapitres d’ analyse-R . Le présent chapitre ne se veut pas exhaustif et ne présente qu’une sélection choisie d’extensions. Le site ggplot2 extensions (http://www.ggplot2-exts.org/) recense diverses extensions pour ggplot2 . Pour une présentation des fonctions de base et des concepts de ggplot2, on pourra se référer au chapitre dédié ainsi qu’au deux chapitres introductifs : introduction à ggplot2 et graphiques bivariés avec ggplot2 . Pour trouver l’inspiration et des exemples de code, rien ne vaut l’excellent site https://www.r-graph-gallery.com/ . Nouvelles géométries Étiquettes non superposées Lorsque l’on affiche des étiquettes de texte, ces dernières peuvent se supperposer lorsqu’elles sont proches. Les géométries geom_text_repel et geom_label_repel de l’extension ggrepel prennent en compte la position des différentes étiquettes pour éviter qu’elles ne se chevauchent. library (ggplot2) library (ggrepel) library (ggrepel) dat <- subset (mtcars, wt > 2.75 & wt < 3.45) dat $ car <- rownames (dat) p <- ggplot (dat) + aes (wt, mpg, label = car) + geom_point (color = "red") p1 <- p + geom_text () + labs (title = "geom_text()") p2 <- p + geom_text_repel () + labs (title = "geom_text_repel()") cowplot :: plot_grid (p1, p2, nrow = 1) Pour plus d’informations : https://ggrepel.slowkow.com/ Graphiques en sucettes (lollipop) L’extension ggalt propose une géométrie geom_lollipop permettant de réaliser des graphiques dit en sucettes . df <- read.csv (text = "category,pct Other,0.09 South Asian/South Asian Americans,0.12 Interngenerational/Generational,0.21 S Asian/Asian Americans,0.25 Muslim Observance,0.29 Africa/Pan Africa/African Americans,0.34 Gender Equity,0.34 Disability Advocacy,0.49 European/European Americans,0.52 Veteran,0.54 Pacific Islander/Pacific Islander Americans,0.59 Non-Traditional Students,0.61 Religious Equity,0.64 Caribbean/Caribbean Americans,0.67 Latino/Latina,0.69 Middle Eastern Heritages and Traditions,0.73 Trans-racial Adoptee/Parent,0.76 LBGTQ/Ally,0.79 Mixed Race,0.80 Jewish Heritage/Observance,0.85 International Students,0.87", stringsAsFactors = FALSE, sep = ",", header = TRUE) library (ggplot2) library (ggalt) library (scales) ggplot (df) + aes (y = reorder (category, pct), x = pct) + geom_lollipop (point.colour = "steelblue", point.size = 2, horizontal = TRUE) + scale_x_continuous (expand = c (0, 0), labels = percent, breaks = seq (0, 1, by = 0.2), limits = c (0, 1)) + labs (x = NULL, y = NULL, title = "SUNY Cortland Multicultural Alumni survey results", subtitle = "Ranked by race, ethnicity, home land and orientation \n among the top areas of concern", caption = "Data from http://stephanieevergreen.com/lollipop/") + theme_minimal () Graphique d’haltères (dumbbell) L’extension ggalt propose une géométrie geom_dumbbell permettant de réaliser des graphiques dit en haltères . library (ggalt) df <- data.frame (trt = LETTERS[1 : 5], l = c (20, 40, 10, 30, 50), r = c (70, 50, 30, 60, 80)) ggplot (df) + aes (y = trt, x = l, xend = r) + geom_dumbbell (size = 3, color = "#e3e2e1", colour_x = "#5b8124", colour_xend = "#bad744") + labs (x = NULL, y = NULL) + theme_minimal () Accolades de comparaison (bracket) La géométrie geom_braket de l’extension ggpubr permets d’ajouter sur un graphique des accolades de comparaison entre groupes. library (ggpubr) df <- ToothGrowth df $ dose <- factor (df $ dose) ggplot (df) + aes (x = dose, y = len) + geom_boxplot () + geom_bracket (xmin = "0.5", xmax = "1", y.position = 30, label = "t-test, p < 0.05") ggplot (df) + aes (x = dose, y = len) + geom_boxplot () + geom_bracket (xmin = c ("0.5", "1"), xmax = c ("1", "2"), y.position = c (30, 35), label = c ("***", "**"), tip.length = 0.01) Plus d’informations : https://rpkgs.datanovia.com/ggpubr/ Diagramme en crêtes (ridges) L’extension ggridges fournit une géométrie geom_density_ridges_gradient pour la création de diagramme en crêtes. library (ggridges) ggplot (lincoln_weather, aes (x = ` Mean Temperature [F] `, y = Month, fill = stat (x))) + geom_density_ridges_gradient (scale = 3, rel_min_height = 0.01) + scale_fill_viridis_c (name = "Temp. [F]", option = "C") + labs (title = "Temperatures in Lincoln NE in 2016") + theme_ridges () Picking joint bandwidth of 3.37 Plus d’informations : https://wilkelab.org/ggridges/ Graphique en gaufres (waffle) L’extension waffle propose geom_waffle pour des graphiques dits en gaufres . ATTENTION : elle s’installe avec la commande install.packages("waffle", repos = "https://cinc.rud.is") . library (waffle) xdf <- data.frame (parts = factor (rep (month.abb[1 : 3], 3), levels = month.abb[1 : 3]), vals = c (10, 20, 30, 6, 14, 40, 30, 20, 10), fct = c (rep ("Thing 1", 3), rep ("Thing 2", 3), rep ("Thing 3", 3))) ggplot (xdf) + aes (fill = parts, values = vals) + geom_waffle () + facet_wrap (~ fct) + scale_fill_manual (name = NULL, values = c ("#a40000", "#c68958", "#ae6056"), labels = c ("Fruit", "Sammich", "Pizza")) + coord_equal () + theme_minimal () + theme_enhance_waffle () Plus d’informations : https://github.com/hrbrmstr/waffle Graphique en mosaïque (mosaic plot) L’extension ggmosaic permets de réaliser des graphiques en mosaïque avec geom_mosaic . library (ggmosaic) ggplot (data = fly) + geom_mosaic (aes (x = product (RudeToRecline), fill = RudeToRecline), na.rm = TRUE) ggplot (data = fly) + geom_mosaic (aes (x = product (DoYouRecline, RudeToRecline), fill = DoYouRecline), na.rm = TRUE) Plus d’informations : https://cran.r-project.org/web/packages/ggmosaic/vignettes/ggmosaic.html Graphique de pirates : alternative aux boîtes à moustache (pirat plot) Cette représentation alternative aux boîtes à moustache s’obtient avec la géométrie geom_pirate de l’extension ggpirate 1 . library (ggplot2) library (ggpirate) ggplot (mpg, aes (x = class, y = cty)) + geom_pirate (aes (colour = class, fill = class)) + theme_bw () Pour plus d’informations : https://github.com/mikabr/ggpirate Axes, légende et facettes Axes limités coord_capped_cart et coord_capped_flip de l’extension lemon permet de limiter le dessin des axes au minimum et au maximum. Voir l’exemple ci-dessous. library (ggplot2) library (lemon) p <- ggplot (mtcars) + aes (x = cyl, y = mpg) + geom_point () + theme_classic () + ggtitle ("Axes classiques") pcapped <- p + coord_capped_cart (bottom = "both", left = "both") + ggtitle ("Axes limités") cowplot :: plot_grid (p, pcapped, nrow = 1) Répéter les étiquettes des axes sur des facettes Lorsque l’on réalise des facettes, les étiquettes des axes ne sont pas répétées. library (ggplot2) ggplot (mpg) + aes (displ, cty) + geom_point () + facet_wrap (~ cyl) L’extension lemon propose facet_rep_grid et facet_rep_wrap qui répètent les axes sur chaque facette. library (lemon) ggplot (mpg) + aes (displ, cty) + geom_point () + facet_rep_wrap (~ cyl, repeat.tick.labels = TRUE) Cartes Voir le chapitre dédié . Graphiques complexes Graphiques divergents L’extension ggcharts fournit plusieurs fonctions de haut niveau pour faciliter la réalisation de graphiques divergents en barres (diverging_bar_chart), en sucettes (diverging_lollipop_chart) voire même une pyramide des âges (pyramid_chart). library (ggcharts) data (mtcars) mtcars_z <- dplyr :: transmute (.data = mtcars, model = row.names (mtcars), hpz = scale (hp)) diverging_bar_chart (data = mtcars_z, x = model, y = hpz) diverging_lollipop_chart (data = mtcars_z, x = model, y = hpz, lollipop_colors = c ("#006400", "#b32134"), text_color = c ("#006400", "#b32134")) data ("popch") pyramid_chart (data = popch, x = age, y = pop, group = sex) Warning: `expand_scale()` is deprecated; use `expansion()`
instead.

Warning: `expand_scale()` is deprecated; use `expansion()`
instead.

Warning: `expand_scale()` is deprecated; use `expansion()`
instead.

Warning: `expand_scale()` is deprecated; use `expansion()`
instead. Graphiques interactifs Voir le chapitre dédie . Graphiques animés L’extension gganimate permets de réaliser des graphiques animés. Voici un exemple : library (ggplot2) library (gganimate) library (gapminder) ggplot (gapminder, aes (gdpPercap, lifeExp, size = pop, colour = country)) + geom_point (alpha = 0.7, show.legend = FALSE) + scale_colour_manual (values = country_colors) + scale_size (range = c (2, 12)) + scale_x_log10 () + facet_wrap (~ continent) + # Here comes the gganimate specific bits labs (title = "Year: {frame_time}", x = "GDP per capita", y = "life expectancy") + transition_time (year) + ease_aes ("linear") Voir le site de l’extension (https://gganimate.com/) pour la documentation et des tutoriels. Il est conseillé d’installer également l’extension gifski avec gganimate . Surligner certaines données L’extension gghighlight fournit une fonction gghiglight qui permets de surligner les données qui remplissent des conditions spécifiées. d <- purrr :: map_dfr (letters, ~ data.frame (idx = 1 : 400, value = cumsum (runif (400, -1, 1)), type = ., flag = sample (c (TRUE, FALSE), size = 400, replace = TRUE), stringsAsFactors = FALSE)) ggplot (d) + aes (x = idx, y = value, colour = type) + geom_line () library (gghighlight) ggplot (d) + aes (x = idx, y = value, colour = type) + geom_line () + gghighlight (max (value) > 20) label_key: type ggplot (iris, aes (Sepal.Length, fill = Species)) + geom_histogram () + gghighlight () + facet_wrap (~ Species) Thèmes et couleurs Palettes de couleurs Voir le chapitre Couleurs et palettes pour une sélection d’extensions proposant des palettes de couleurs additionnelles. hrbrthemes L’extension hrbrthemes fournit plusieurs thèmes graphiques pour ggplot2 . Un exemple ci-dessous. Pour plus d’informations, voir https://github.com/hrbrmstr/hrbrthemes . library (ggplot2) library (hrbrthemes) ggplot (mtcars, aes (mpg, wt)) + geom_point (aes (color = factor (carb))) + labs (x = "Fuel efficiency (mpg)", y = "Weight (tons)", title = "Seminal ggplot2 scatterplot example", subtitle = "A plot that is only useful for demonstration purposes", caption = "Brought to you by the letter 'g'") + scale_color_ipsum () + theme_ipsum_rc () ggthemes ggthemes propose une vingtaine de thèmes différentes présentés sur le site de l’extension : https://jrnold.github.io/ggthemes/ . Voir ci-dessous un exemple du thème theme_tufte inspiré d’Edward Tufte. library (ggplot2) library (ggthemes) p <- ggplot (mtcars, aes (x = wt, y = mpg)) + geom_point () + scale_x_continuous (breaks = extended_range_breaks ()(mtcars $ wt)) + scale_y_continuous (breaks = extended_range_breaks ()(mtcars $ mpg)) + ggtitle ("Cars") p + geom_rangeframe () + theme_tufte () p + geom_rug () + theme_tufte (ticks = FALSE) Combiner plusieurs graphiques Voir le chapitre dédié . Cette extension n’étant pas sur CRAN, on l’installera avec la commande devtools::install_github("mikabr/ggpirate") . Combiner plusieurs graphiques multiplot (JLutils) plot_grid (cowplot) patchwork Légende partagée entre plusieurs graphiques Vous savez réaliser des graphiques avec ggplot2 ? Il est très facile de combiner plusieurs graphiques en un seul. multiplot (JLutils) Dans son ouvrage Cookbook for R, Winston Chang propose une fonction multiplot pour combiner plusieurs graphiques 1 L’extension JLutils disponible sur GitHub propose une version améliorée de cette fonction. Pour installer JLutils si non disponible sur votre PC, copier/coller le code ci-dessous. if (! require (devtools)) {install.packages ("devtools") library (devtools)} install_github ("larmarange/JLutils") Commençons par créer quelques graphiques avec ggplot2 . library (ggplot2) p1 <- ggplot (mtcars, aes (wt, mpg)) + geom_point () p2 <- ggplot (mtcars, aes (factor (cyl))) + geom_bar () p3 <- ggplot (mtcars, aes (factor (cyl), mpg)) + geom_violin () p4 <- ggplot (mtcars, aes (factor (cyl), mpg)) + geom_boxplot () Parce que quelques exemples valent mieux qu’un long discours. library (JLutils) multiplot (p1, p2, p3, p4) multiplot (p1, p2, p3, p4, cols = 2) multiplot (p1, p2, p3, layout = matrix (c (1, 2, 3, 3), nrow = 2)) multiplot (p1, p2, p3, layout = matrix (c (1, 2, 3, 3), nrow = 2, byrow = TRUE)) multiplot (p1, p2, p3, layout = matrix (c (1, 2, 3, 3), nrow = 2, byrow = TRUE), heights = c (3, 1)) plot_grid (cowplot) L’extension cowplot propose une fonction équivalente, plot_grid . Son usage est expliqué en détail dans la vignette dédiée inclue avec l’extension : https://cran.r-project.org/web/packages/cowplot/vignettes/plot_grid.html . library (cowplot) ******************************************************** Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot()) ******************************************************** Attaching package: 'cowplot' The following object is masked from 'package:ggpubr':

get_legend The following object is masked from 'package:lubridate':

stamp The following object is masked from 'package:JLutils':

get_legend # simple grid plot_grid (p1, p2, p3, p4) # simple grid with labels and aligned plots plot_grid (p1, p2, p3, p4, labels = c ("A", "B", "C", "D"), align = "hv") # manually setting the number of rows, auto-generate # upper-case labels plot_grid (p1, p2, p3, nrow = 3, labels = "AUTO", label_size = 12, align = "v") # making rows and columns of different widths/heights plot_grid (p1, p2, p3, p4, align = "hv", rel_heights = c (2, 1), rel_widths = c (1, 2)) On notera en passant que le chargement de cowplot modifie le style par défaut des graphiques ggplot2 . Voir https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html . patchwork Citons également l’extension patchwork, disponible sur GitHub (https://github.com/thomasp85/patchwork) qui propose une syntaxe un petit peu différente, par addition de graphiques. library (patchwork) Attaching package: 'patchwork' The following object is masked from 'package:cowplot':

align_plots p1 + (p2 + p3) + p4 + plot_layout (ncol = 1) Légende partagée entre plusieurs graphiques JLutils et cowplot fournissent tous deux une fonction get_legend permettant d’extraire la légende d’un graphique puis de l’utiliser avec multiplot ou plot_grid . Créons quelques graphiques. dsamp <- diamonds[sample (nrow (diamonds), 1000), ] p1 <- qplot (carat, price, data = dsamp, colour = clarity) + theme (plot.margin = unit (c (6, 0, 6, 0), "pt")) p2 <- qplot (depth, price, data = dsamp, colour = clarity) + theme (plot.margin = unit (c (6, 0, 6, 0), "pt")) + ylab ("") p3 <- qplot (color, price, data = dsamp, colour = clarity) + theme (plot.margin = unit (c (6, 0, 6, 0), "pt")) + ylab ("") Récupérons la légende du premier graphique graphique puis supprimons là dans les trois graphiques. leg <- get_legend (p1) p1 <- p1 + theme (legend.position = "none") p2 <- p2 + theme (legend.position = "none") p3 <- p3 + theme (legend.position = "none") Combinons le tout. multiplot (p1, p2, p3, leg, cols = 2) plot_grid (p1, p2, p3, leg, ncol = 2) Enfin, citons également la fonction grid_arrange_shared_legend de l’extension lemon 2 . Voir http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/ . lemon fournit également diverses fonctions pour manipuler des graphiques ggplot2, comme par exemple la possibilité de répéter les axes quand on utilise des facettes. Graphiques interactifs ggplotly (plotly) ggvis ggplotly (plotly) Vous savez réaliser des graphiques avec ggplot2 ? Vous savez faire un graphique interactif. Rien de plus facile avec la fonction ggplotly de l’extension plotly . Créons un graphique. library (ggplot2) p <- ggplot (iris) + aes (x = Petal.Width, y = Sepal.Length, color = Species) + geom_point () Voici son rendu classique avec ggplot2 . p Et si on passe notre graphique à ggplotly . (N’hésitez pas à faire passer le curseur de votre souris sur le graphique.) library (plotly) Attaching package: 'plotly' The following object is masked from 'package:MASS':

select The following object is masked from 'package:Hmisc':

subplot The following objects are masked from 'package:plyr':

arrange, mutate, rename, summarise The following object is masked from 'package:ggplot2':

last_plot The following object is masked from 'package:stats':

filter The following object is masked from 'package:graphics':

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(y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]} Une documentation complète sur ggplotly est disponible sur https://plot.ly/ggplot2/ . ggvis Il existe également une extension ggvis dédiée aux graphiques interactifs. Sa documentation complète est disponible sur https://ggvis.rstudio.com/ . lattice : graphiques et formules Les bases des graphiques lattice De l’intérêt des formules R Les formules R Principaux types de graphiques avec lattice (et ggplot2) Histogramme Courbe de densité Diagramme en barres Diagramme de type boîtes à moustaches Diagramme en points Diagramme de dispersion Bien que l’on ait fait le choix de présenter principalement l’extension ggplot2 plutôt que l’extension lattice, celle-ci reste un excellent choix pour la visualisation, notamment, de panels et de séries temporelles . On trouve de très beaux exemples d’utilisation de lattice en ligne, mais un peu moins de documentation, et beaucoup moins d’extensions, que pour ggplot2 . On peut trouver en ligne un support de cours détaillé (en anglais) de Deepayan Sarkar (https://www.isid.ac.in/~deepayan/R-tutorials/labs/04_lattice_lab.pdf), également l’auteur de l’ouvrage Lattice: Multivariate Data Visualization with R (http://lmdvr.r-forge.r-project.org/). Les bases des graphiques lattice R dispose de deux principaux systèmes graphiques : un système de base contrôlé par le package graphics et le système grid, sur lequel se basent à la fois les packages lattice et ggplot2 . Ce système fournit les mêmes fonctionnalités de base que graphics mais offre une gestion de l’arrangement des objets graphiques plus développée, et surtout la possibilité d’utiliser ce que l’on appelle des graphiques en treillis . De plus, les graphiques peuvent être mis à jour très simplement, disposent de thèmes de couleur pré-définis, et offrent un certain degré d’interactivité, avec ou sans le package plotly . Enfin, la syntaxe est plus homogène et grandement simplifié, grâce à l’usage de formules. De l’intérêt des formules R Voici par exemple comment afficher la courbe de densité (i.e., la version continue et “lissée” d’un histogramme) de deux séries d’observations définies par les niveaux du facteur supp dans le data frame ToothGrowth, disponible dans les exemples de base de R . Notons que l’on souhaite également faire apparaître les distributions univariées, un peu à l’image de ce que fournit rug . Or cette fonction ne permet pas d’exploiter une variable de groupement, donc il sera nécessaire de gérer tout cela manuellement. Voici les instructions permettant de générer le graphique désiré : plot (density (ToothGrowth $ len[ToothGrowth $ supp == "OJ"]), main = "", xlab = "len", las = 1, lwd = 2, col = "coral") lines (density (ToothGrowth $ len[ToothGrowth $ supp == "VC"]), lwd = 2, col = "cornflowerblue") points (x = ToothGrowth $ len[ToothGrowth $ supp == "OJ"], y = runif (length (ToothGrowth $ len[ToothGrowth $ supp == "OJ"]), min = -0.001, max = 0.001), col = "coral") points (x = ToothGrowth $ len[ToothGrowth $ supp == "VC"], y = runif (length (ToothGrowth $ len[ToothGrowth $ supp == "VC"]), min = -0.001, max = 0.001), col = "cornflowerblue") legend ("top", levels (ToothGrowth $ supp), col = c ("coral", "cornflowerblue"), lty = 1, bty = "n") Courbes de densité avec les graphiques de base Il y a plusieurs points à retenir dans les instructions ci-dessus : (1) il est nécessaire de définir les deux courbes de densité et les deux distributions univariées, en prenant garde à bien indiquer comment sélectionner les observations (OJ ou VC) en préfixant systématiquement le nom des variables par le nom du data frame ; (2) la définition des couleurs se fait manuellement et si l’on souhaite changer de thème de couleur, il faudra mettre à jour l’ensemble des instructions, en prenant garde à ce que la lgende reste synchronisée avec les courbes de densité et les nuages de points ; et, bien entendu, (3) il est nécessaire de gérer soi-même la légende, ce qui signifie se rappeler les couleurs et l’ordre des niveaux du facteur considéré, ainsi que les axes graphiques dans le cas où l’on souhaite les maintenir coordonnés sur plusieurs panneaux graphiques. Voici le même graphique avec lattice : library (lattice) densityplot (~ len, data = ToothGrowth, group = supp, auto.key = TRUE) Courbes de densité avec le package lattice Avec ggplot2, cela donnerait : library (ggplot2) ggplot (data = ToothGrowth, aes (x = len, color = supp)) + geom_line (stat = "density") + geom_rug () Courbes de densité avec le package ggplot2 Clairement, on gagne en nombre d’instructions à taper dans la console et en clarté d’expression également, grâce notamment à l’usage de formules permettant de décrire la relation entre chacune des variables utilisées pour construire la représentation graphique. Les formules R Les formules utilisées dans le système lattice sont presque identiques à celles retouvées dans les modèles d’analyse de variance (aov) ou de régression (lm). En réalité, la notation par formule qu’utilise R est celle proposée par Wilkinson et coll. dans les années 70 pour schématiser la relation entre plusieurs variables dans un plan d’expérience. Plus spécifiquement, l’idée revient à exprimer une relation fonctionnelle, symbolise´e par l’opérateur ~, entre une variable re´ponse y et une ou plusieurs variables explicatives. Disons, pour simplifier, que y est une variable numérique, de même que x, et que a et b sont des variables catégorielles (des facteurs dans le langage R). Voici les principales relations auxquelles on peut s’intéresser dans un modèle statistique linéaire : y ~ x : régression linéaire simple, y ~ x + 0 : idem avec suppression du terme d’ordonnée à l’origine, y ~ a + b : ANOVA avec deux effets principaux, y ~ a * b : idem avec interaction (équivalent à 1 + a + b + a:b), y ~ a / b : idem en considérant une relation d’emboîtement (équivalent à 1 + a + b + a %in% b). Un exemple typique d’utilisation pour un modèle d’ANOVA à trois facteurs est donné ci-dessous : fm <- y ~ a * b * c # modèle de base (A, B, C, AB, AC, BC, ABC) mod1 <- aov (fm, data = dfrm) # estimation des parame`tres du mode`le update (mod1, . ~ . - a : b : c) # suppression de l'interaction ABC Quant on y réfléchit un peu, les relations ci-dessus peuvent très bien s’appliquer au cas de la composition graphique : y ~ x signifie dans ce cas que l’on souhaite représenter l’évolution de y en fonction de x . En d’autres termes, on s’intéresse à un nuage de dispersion. Le package lattice ajoute les notations suivantes : ~ x : dans le cas où l’on ne décrit qu’une seule variable (i.e., sa distribution), a | b : dans le cas où l’on considère la variable a, conditionnellement à la variable b, c’est-à-dire les niveaux de a pour chacun des niveaux de b (ce qui revient à l’interaction a:b citée ci-dessus). Cette dernière notation se révèlera être très utile dans le cas des représentations graphiques conditionnelles, par exemple lorsque l’on souhaite afficher la distribution d’une variable numérique dans différents groupes d’individus définis par les niveaux d’une variable catégorielle, ou lorsque l’on souhaite surligner d’une couleur différentes les points d’un diagramme de dispersion selon la valeur prise par une troisième variable. Les formules R sont omniprésentes dans les modèles statistiques, dans les graphiques, mais également dans certaines commandes d’agrégation. Au bout du compte, avec une même formule il est possible de calculer des moyennes de groupes, réaliser une ANOVA et construire la représentation graphique associée. En voici un exemple : fm <- len ~ supp m <- aggregate (fm, data = ToothGrowth, mean) summary (aov (fm, data = ToothGrowth)) bwplot (fm, data = ToothGrowth) Principaux types de graphiques avec lattice (et ggplot2) Même si le package lattice fournit moins de commandes que ggplot2, il n’en demeure pas moins qu’il est tout aussi facile de réaliser des représentations graphiques simples en un tour de main. Voici quelques exemples de représentations graphiques uni- et bivariées. Les données d’illustration sont les mêmes que celles utilisées plus haut (ToothGrowth): il s’agit d’une expérience de biologie dans laquelle on s’intéresse à la croissance des odontoblastes de cochons d’inde quantifiée par leur longueur (variable len) lorsqu’on administre à ces derniers de la vitamine C soit sous forme d’acide ascorbique soit sous forme de jus d’orange (supp, OJ = jus d’orange), à différentes doses (dose, en mg). Histogramme Un histogramme d’effectifs se construit avec histogram . Puisqu’il s’agit de décrire une seule variable, ou sa distribution plus précisément, la formule à employer ne contient pas de variable à gauche du symbole ~ et l’on se contente d’écrire la variable à résumer à droite dans la formule : histogram (~ len, data = ToothGrowth, type = "count") Histogramme d’effectifs L’option type = "count" permet de forcer la représentation sous forme d’effectifs puisque, par défaut, c’est la densité qui est représentée. La formulation équivalente sous ggplot2 serait : ggplot (data = ToothGrowth, aes (x = len)) + geom_histogram (binwidth = 5) (Ou alors qplot(x = len, data = ToothGrowth, geom = "histogram", binwidth = 5) .) En ajoutant une facette pour tenir compte de la variable supp, cela donne : histogram (~ len | supp, data = ToothGrowth, breaks = seq (0, 40, by = 5)) Histogramme d’effectifs conditionné sur une variable catégorielle Avec ggplot2, les facettes sont gérées grâce aux commandes facet_grid et facet_wrap . Courbe de densité Une courbe de densité se construit à l’aide de densityplot et la syntaxe est strictement identique à celle de histogram, à l’option type= près. densityplot (~ len, data = ToothGrowth, plot.points = FALSE, from = 0, to = 40) Courbe de densité Il est possible de régler le paramètre de lissage à l’aide de l’option bw= : des valeurs plus élevées résultent en une courbe beaucoup plus lissée (essayez avec bw = 10!) et donc beaucoup moins sensible aux variations locales de la densité. À ce stade, on peut en profiter pour discuter les options de conditionnement sur une variable catégorielle et la manière de gérer la présentation graphique : dans le cas d’un histogramme, il est délicat de superposer deux distributions ou plus sur le même graphique, même en ajoutant de la transparence, d’où l’idée de représenter les distributions dans des panneaux graphiques séparés. C’est ce qu’on a réalisé en indiquant que l’on souhaitait décrire la variable len conditionnellement aux valeurs prises par supp (~ len | supp). Dans ce cas, l’opérateur | invoque une facette et un decoupage en autant de panneaux graphiques qu’il y a de valeurs uniques dans la variable supp . Une autre approche consiste à utiliser l’option groups=, et dans ce cas les différentes distributions seront affichées dans le même panneau graphique. Dans le cas d’une courbe de densité, cela revient à les superposer sur la même fenêtre graphique, avec un système de coordonnées unique. Les deux options de conditionnement peuvent être combinées naturellement. Voici un exemple de graphique conditionnel un peu plus élaboré : densityplot (~ len, data = ToothGrowth, groups = supp, auto.key = TRUE, xlab = "len", par.settings = list (superpose.line = list (col = c ("coral", "cornflowerblue")))) Courbe de densité conditionnelle Au passage, on en a profité pour modifier le thème de couleur. Notez qu’en utilisant par.settings=, lattice se charge de coordonner les couleurs de la légende (auto.key = TRUE) avec celle des éléments graphiques correspondants. L’équivalent sous ggplot2 revient à peu près à l’instruction suivante : ggplot (data = ToothGrowth) + aes (x = len, colour = supp) + geom_line (stat = "density") + expand_limits (x = c (0, 40)) + scale_colour_manual ("", values = c ("coral", "cornflowerblue")) + theme_bw () Diagramme en barres Les diagrammes en barres peuvent avantageusement être remplacés par des diagrammes en points, tels que les diagrammes de Cleveland (cf. plus loin), mais en attendant voici comment en réaliser un à l’aide de barchart à partir de données agrégées : library (latticeExtra, quietly = TRUE) Attaching package: 'latticeExtra' The following object is masked from 'package:ggplot2':

layer m <- aggregate (len ~ supp + dose, data = ToothGrowth, mean) barchart (len ~ dose, data = m, groups = supp, horizontal = FALSE, auto.key = TRUE, par.settings = ggplot2like ()) Diagramme en barres Notons que par.settings= permet non seulement de fournir des options additionnelles pour contrôler le rendu des éléments graphiques (couleur, type de ligne ou de symboles, etc.) mais également d’utiliser des thèmes graphiques disponibles dans le package latticeExtra . Diagramme de type boîtes à moustaches Les diagrammes en forme de boîtes à moustaches sont obtenus à l’aide de la commande bwplot . Voici un exemple d’utilisation : bwplot (len ~ supp, data = ToothGrowth, pch = "|") Diagramme en forme de boîtes à moustaches L’option pch= permet de contrôler la manière dont la médiane est figurée dans la boîte. Par défaut il s’agit d’un simple point, mais si l’on souhaite utiliser les représentations plus classiques, telles que celles trouvées dans boxplot ou geom_boxplot, il suffit de suivre l’exemple ci-dessus. Notons que dans le cas de cette représentation graphique, le conditionnement sur la variable supp est d’emblée réalisé par l’utilisation d’une formule invoquant la variable de conditionnement à droite de l’opérateur ~ . Diagramme en points Le même type de représentation graphique peut être obtenu en utilisant directement les données individuelles, et non leur résumé en cinq points (tel que fournit par summary et exploité par bwplot). Dans ce cas, il s’agit de la commande dotplot, qui permet de construire des diagrammes de Cleveland (moyenne ou effectif total calculé pour une variable en fonction des niveaux d’une autre variable) ou, dans le cas où la variable à résumer consiste en une série de mesures individuelles numériques, des diagrammes de dispersion. Voici une illustration pour ce dernier cas de figure : dotplot (len ~ supp, ToothGrowth, jitter.x = TRUE) Diagramme en points Diagramme de dispersion Enfin, un diagramme de dispersion est construit à l’aide de la commande xyplot . xyplot (len ~ dose, ToothGrowth, type = c ("p", "smooth")) Diagramme de dispersion Même si l’exemple ne s’y prête guère, on en a profité pour ajouter une courbe lowess de régression afin d’indiquer la tendance de covariation entre les deux variables numériques. L’aide en ligne pour xyplot n’est pas très utile dans ce cas, et il faut en fait aller regarder les options de personnalisation disponibles dans la sous-fonction correspondante : panel.xyplot . Cartes Pour une présentation de l’analyse spatiale sous R, se référer au chapitre dédié . Il existe de multiple approches pour réaliser des cartes sous R, y compris avec ggplot2, mais également de manière native avec les extensions sp et sf . Il existe également des extensions apportant des fonctionalités additionnelles comme ggmap, mapview ou encore tmap . Pour une introduction succincte en français, on pourra se référer à la section 5.4 du spport de cours Logiciel R et programmation d’Ewan Gallic. Voir également l’excellente présentation Données géospatiales et cartographie avec R de Nicolas Roelandt : https://roelandtn.frama.io/slides/2090628_meetup_Raddict_datageo.html . En complément (en anglais), la vignette Plotting Simple Features de l’extension sf ou encore le chapitre Making maps with R de l’ouvrage Geocomputation with R de Robin Lovelace, Jakub Nowosad et Jannes Muenchow. Autres extensions graphiques GGally ggpubr ggdendro circlize Diagrammes de Sankey DiagrammeR highcharter .section{opacity: 1 !important;} /* contrer mermaid */ Pour trouver l’inspiration et des exemples de code, rien ne vaut l’excellent site https://www.r-graph-gallery.com/ . GGally L’extension GGally, déjà abordée dans d’autres chapitres, fournit plusieurs fonctions graphiques d’exploration des résultats d’un modèle ou des relations entre variables. reg <- lm (Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris) library (GGally) ggcoef (reg) data (tips, package = "reshape") ggpairs (tips) Plus d’information : https://ggobi.github.io/ggally/ ggpubr L’extension ggpubr fournit plusieurs fonctions pour produire clés en main différents graphiques bivariés avec une mise en forme allégée. library (ggpubr) data ("ToothGrowth") df <- ToothGrowth ggboxplot (df, x = "dose", y = "len", color = "dose", palette = c ("#00AFBB", "#E7B800", "#FC4E07"), add = "jitter", shape = "dose") data ("mtcars") dfm <- mtcars # Convert the cyl variable to a factor dfm $ cyl <- as.factor (dfm $ cyl) # Add the name colums dfm $ name <- rownames (dfm) # Calculate the z-score of the mpg data dfm $ mpg_z <- (dfm $ mpg - mean (dfm $ mpg)) / sd (dfm $ mpg) dfm $ mpg_grp <- factor (ifelse (dfm $ mpg_z < 0, "low", "high"), levels = c ("low", "high")) ggbarplot (dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in ascending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", xlab = FALSE, legend.title = "MPG Group") ggdotchart (dfm, x = "name", y = "mpg_z", color = "cyl", # Color by groups palette = c ("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots add.params = list (color = "lightgray", size = 2), # Change segment color and size group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round (dfm $ mpg_z, 1), # Add mpg values as dot labels font.label = list (color = "white", size = 9, vjust = 0.5), # Adjust label parameters ggtheme = theme_pubr () # ggplot2 theme) + geom_hline (yintercept = 0, linetype = 2, color = "lightgray") Plus d’informations : https://rpkgs.datanovia.com/ggpubr/ ggdendro L’extension ggendro avec sa fonction ggdendrogram permet de représenter facilement des dendrogrammes avec ggplot2 . library (ggplot2) library (ggdendro) hc <- hclust (dist (USArrests), "ave") hcdata <- dendro_data (hc, type = "rectangle") ggplot () + geom_segment (data = segment (hcdata), aes (x = x, y = y, xend = xend, yend = yend)) + geom_text (data = label (hcdata), aes (x = x, y = y, label = label, hjust = 0), size = 3) + coord_flip () + scale_y_reverse (expand = c (0.2, 0)) ### demonstrate plotting directly from object class hclust ggdendrogram (hc) ggdendrogram (hc, rotate = TRUE) Plus d’informations : https://cran.r-project.org/web/packages/ggdendro/vignettes/ggdendro.html circlize L’extension circlize est l’extension de référence quand il s’agit de représentations circulaires. Un ouvrage entier lui est dédié : https://jokergoo.github.io/circlize_book/book/ . Voici un exemple issu de https://www.data-to-viz.com/story/AdjacencyMatrix.html . library (tidyverse) -- Attaching packages -------------------------------------------------- tidyverse 1.3.0 -- v tibble 3.0.1 v purrr 0.3.4
v readr 1.3.1 v forcats 0.5.0 -- Conflicts ----------------------------------------------------- tidyverse_conflicts() --
x dplyr::arrange() masks plyr::arrange()
x lubridate::as.difftime() masks base::as.difftime()
x dplyr::between() masks data.table::between()
x purrr::compact() masks plyr::compact()
x dplyr::count() masks plyr::count()
x lubridate::date() masks base::date()
x tidyr::extract() masks magrittr::extract()
x dplyr::failwith() masks plyr::failwith()
x dplyr::filter() masks stats::filter()
x dplyr::first() masks data.table::first()
x lubridate::hour() masks data.table::hour()
x dplyr::id() masks plyr::id()
x lubridate::intersect() masks base::intersect()
x lubridate::isoweek() masks data.table::isoweek()
x dplyr::lag() masks stats::lag()
x dplyr::last() masks data.table::last()
x lubridate::mday() masks data.table::mday()
x lubridate::minute() masks data.table::minute()
x lubridate::month() masks data.table::month()
x ggpubr::mutate() masks dplyr::mutate(), plyr::mutate()
x lubridate::quarter() masks data.table::quarter()
x dplyr::rename() masks plyr::rename()
x lubridate::second() masks data.table::second()
x purrr::set_names() masks magrittr::set_names()
x lubridate::setdiff() masks base::setdiff()
x dplyr::summarise() masks plyr::summarise()
x dplyr::summarize() masks plyr::summarize()
x purrr::transpose() masks data.table::transpose()
x lubridate::union() masks base::union()
x lubridate::wday() masks data.table::wday()
x lubridate::week() masks data.table::week()
x lubridate::yday() masks data.table::yday()
x lubridate::year() masks data.table::year() # Load data data <- read.table ("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/13_AdjacencyDirectedWeighted.csv", header = TRUE) # short names colnames (data) <- c ("Africa", "East Asia", "Europe", "Latin Ame.", "North Ame.", "Oceania", "South Asia", "South East Asia", "Soviet Union", "West.Asia") rownames (data) <- colnames (data) # I need a long format data_long <- data %>% rownames_to_column %>% gather (key = "key", value = "value", - rowname) library (circlize) ========================================
circlize version 0.4.8
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: http://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
======================================== # parameters circos.clear () circos.par (start.degree = 90, gap.degree = 4, track.margin = c (- 0.1, 0.1), points.overflow.warning = FALSE) par (mar = rep (0, 4)) # color palette library (viridis) Loading required package: viridisLite mycolor <- viridis (10, alpha = 1, begin = 0, end = 1, option = "D") mycolor <- mycolor[sample (1 : 10)] # Base plot chordDiagram (x = data_long, grid.col = mycolor, transparency = 0.25, directional = 1, direction.type = c ("arrows", "diffHeight"), diffHeight = -0.04, annotationTrack = "grid", annotationTrackHeight = c (0.05, 0.1), link.arr.type = "big.arrow", link.sort = TRUE, link.largest.ontop = TRUE) # Add text and axis circos.trackPlotRegion (track.index = 1, bg.border = NA, panel.fun = function (x, y) {xlim = get.cell.meta.data ("xlim") sector.index = get.cell.meta.data ("sector.index") # Add names to the sector. circos.text (x = mean (xlim), y = 3.2, labels = sector.index, facing = "bending", cex = 0.8) # Add graduation on axis circos.axis (h = "top", major.at = seq (from = 0, to = xlim[2], by = ifelse (test = xlim[2] > 10, yes = 2, no = 1)), minor.ticks = 1, major.tick.percentage = 0.5, labels.niceFacing = FALSE)}) Diagrammes de Sankey Les diagrammes de Sankey sont un type alternatif de représentation de flux. Voici un premier exemple, qui reprend les données utilisées pour le diagramme circulaire précédent, avec la fonction sankeyNetwork de l’extension sankeyNetwork . # Package library (networkD3) # I need a long format data_long <- data %>% rownames_to_column %>% gather (key = "key", value = "value", - rowname) %>% filter (value > 0) colnames (data_long) <- c ("source", "target", "value") data_long $ target <- paste (data_long $ target, " ", sep = "") # From these flows we need to create a node data frame: it # lists every entities involved in the flow nodes <- data.frame (name = c (as.character (data_long $ source), as.character (data_long $ target)) %>% unique ()) # With networkD3, connection must be provided using id, not # using real name like in the links dataframe.. So we need to # reformat it. data_long $ IDsource = match (data_long $ source, nodes $ name) - 1 data_long $ IDtarget = match (data_long $ target, nodes $ name) - 1 # prepare colour scale ColourScal = "d3.scaleOrdinal() .range([\" #FDE725FF \", \" #B4DE2CFF \", \" #6DCD59FF \", \" #35B779FF \", \" #1F9E89FF \", \" #26828EFF \", \" #31688EFF \", \" #3E4A89FF \", \" #482878FF \", \" #440154FF \"])" # Make the Network sankeyNetwork (Links = data_long, Nodes = nodes, Source = "IDsource", Target = "IDtarget", Value = "value", NodeID = "name", sinksRight = FALSE, colourScale = ColourScal, nodeWidth = 40, fontSize = 13, nodePadding = 20) {"x":{"links":{"source":[0,1,2,3,0,1,4,5,6,7,2,3,8,9,5,6,0,1,5,2,3,9,0,1,7,2,3,2,1,3,8,0,1,2,9],"target":[10,11,11,11,12,12,12,12,12,12,12,12,12,12,13,13,14,14,14,14,14,14,15,15,15,15,15,16,17,17,18,19,19,19,19],"value":[3.142471,1.630997,0.525881,0.145264,2.107883,0.601265,2.401476,1.762587,1.215929,0.17037,1.390272,0.468762,0.60923,0.449623,0.879198,0.276908,0.540887,0.97306,3.627847,1.508008,1.057904,0.169274,0.155988,0.333608,0.190706,0.34742,0.278746,1.307907,0.380388,0.781316,1.870501,0.673004,0.869311,4.902081,0.927243]},"nodes":{"name":["Africa","East Asia","South Asia","South East Asia","Europe","Latin Ame.","North Ame.","Oceania","Soviet Union","West.Asia","Africa ","East Asia ","Europe ","Latin Ame. ","North Ame. ","Oceania ","South Asia ","South East Asia ","Soviet Union ","West.Asia "],"group":["Africa","East Asia","South Asia","South East Asia","Europe","Latin Ame.","North Ame.","Oceania","Soviet Union","West.Asia","Africa ","East Asia ","Europe ","Latin Ame. ","North Ame. ","Oceania ","South Asia ","South East Asia ","Soviet Union ","West.Asia "]},"options":{"NodeID":"name","NodeGroup":"name","LinkGroup":null,"colourScale":"d3.scaleOrdinal() .range([\"#FDE725FF\",\"#B4DE2CFF\",\"#6DCD59FF\",\"#35B779FF\",\"#1F9E89FF\",\"#26828EFF\",\"#31688EFF\",\"#3E4A89FF\",\"#482878FF\",\"#440154FF\"])","fontSize":13,"fontFamily":null,"nodeWidth":40,"nodePadding":20,"units":"","margin":{"top":null,"right":null,"bottom":null,"left":null},"iterations":32,"sinksRight":false}},"evals":[],"jsHooks":[]} Une alternative possible est fournie par l’extension ggalluvial et ses géométries geom_alluvium et geom_stratum . library (ggalluvial) ggplot (data = as.data.frame (Titanic)) + aes (axis1 = Class, axis2 = Sex, axis3 = Age, y = Freq) + scale_x_discrete (limits = c ("Class", "Sex", "Age"), expand = c (0.1, 0.05)) + xlab ("Demographic") + geom_alluvium (aes (fill = Survived)) + geom_stratum () + geom_text (stat = "stratum", infer.label = TRUE) + theme_minimal () Mentionnons également l’extension riverplot pour la création de diagrammes de Sankey. DiagrammeR DiagrammeR est dédiée à la réalisation de diagrammes en ayant recours à la syntaxe Graphviz (via la fonction grViz) ou encore à la syntaxe Mermaid (via la fonction mermaid). library (DiagrammeR) grViz (" digraph boxes_and_circles {# a 'graph' statement graph [overlap = true, fontsize = 10] # several 'node' statements node [shape = box, fontname = Helvetica] A; B; C; D; E; F node [shape = circle, fixedsize = true, width = 0.9] // sets as circles 1; 2; 3; 4; 5; 6; 7; 8 # several 'edge' statements A->1 B->2 B->3 B->4 C->A 1->D E->A 2->4 1->5 1->F E->6 4->6 5->7 6->7 3->8} ") {"x":{"diagram":"\ndigraph boxes_and_circles {\n\n # a \"graph\" statement\n graph [overlap = true, fontsize = 10]\n\n # several \"node\" statements\n node [shape = box,\n fontname = Helvetica]\n A; B; C; D; E; F\n\n node [shape = circle,\n fixedsize = true,\n width = 0.9] // sets as circles\n 1; 2; 3; 4; 5; 6; 7; 8\n\n # several \"edge\" statements\n A->1 B->2 B->3 B->4 C->A\n 1->D E->A 2->4 1->5 1->F\n E->6 4->6 5->7 6->7 3->8\n}\n","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]} mermaid (" graph LR A(Rounded)-->B[Rectangular] B-->C{A Rhombus} C-->D[Rectangle One] C-->E[Rectangle Two] ") {"x":{"diagram":"\ngraph LR\nA(Rounded)-->B[Rectangular]\nB-->C{A Rhombus}\nC-->D[Rectangle One]\nC-->E[Rectangle Two]\n"},"evals":[],"jsHooks":[]} mermaid (" sequenceDiagram customer->>ticket seller: ask ticket ticket seller->>database: seats alt tickets available database->>ticket seller: ok ticket seller->>customer: confirm customer->>ticket seller: ok ticket seller->>database: book a seat ticket seller->>printer: print ticket else sold out database->>ticket seller: none left ticket seller->>customer: sorry end ") {"x":{"diagram":"\nsequenceDiagram\n customer->>ticket seller: ask ticket\n ticket seller->>database: seats\n alt tickets available\n database->>ticket seller: ok\n ticket seller->>customer: confirm\n customer->>ticket seller: ok\n ticket seller->>database: book a seat\n ticket seller->>printer: print ticket else sold out\n database->>ticket seller: none left\n ticket seller->>customer: sorry\n end\n"},"evals":[],"jsHooks":[]} mermaid (" gantt dateFormat YYYY-MM-DD title Adding GANTT diagram functionality to mermaid section A section Completed task :done, des1, 2014-01-06,2014-01-08 Active task :active, des2, 2014-01-09, 3d Future task : des3, after des2, 5d Future task2 : des4, after des3, 5d section Critical tasks Completed task in the critical line :crit, done, 2014-01-06,24h Implement parser and jison :crit, done, after des1, 2d Create tests for parser :crit, active, 3d Future task in critical line :crit, 5d Create tests for renderer :2d Add to mermaid :1d section Documentation Describe gantt syntax :active, a1, after des1, 3d Add gantt diagram to demo page :after a1 , 20h Add another diagram to demo page :doc1, after a1 , 48h section Last section Describe gantt syntax :after doc1, 3d Add gantt diagram to demo page :20h Add another diagram to demo page :48h ") {"x":{"diagram":"\ngantt\n dateFormat YYYY-MM-DD\n title Adding GANTT diagram functionality to mermaid\n\n section A section\n Completed task :done, des1, 2014-01-06,2014-01-08\n Active task :active, des2, 2014-01-09, 3d\n Future task : des3, after des2, 5d\n Future task2 : des4, after des3, 5d\n\n section Critical tasks\n Completed task in the critical line :crit, done, 2014-01-06,24h\n Implement parser and jison :crit, done, after des1, 2d\n Create tests for parser :crit, active, 3d\n Future task in critical line :crit, 5d\n Create tests for renderer :2d\n Add to mermaid :1d\n\n section Documentation\n Describe gantt syntax :active, a1, after des1, 3d\n Add gantt diagram to demo page :after a1 , 20h\n Add another diagram to demo page :doc1, after a1 , 48h\n\n section Last section\n Describe gantt syntax :after doc1, 3d\n Add gantt diagram to demo page :20h\n Add another diagram to demo page :48h\n"},"evals":[],"jsHooks":[]} Plus d’informations : https://rich-iannone.github.io/DiagrammeR/ highcharter L’extension highcharter permet de réaliser des graphiques HTML utilisant la librairie Javascript Highcharts.js . library ("highcharter") data (diamonds, mpg, package = "ggplot2") hchart (mpg, "scatter", hcaes (x = displ, y = hwy, group = class)) {"x":{"hc_opts":{"title":{"text":null},"yAxis":{"title":{"text":"hwy"},"type":"linear"},"credits":{"enabled":false},"exporting":{"enabled":false},"plotOptions":{"series":{"label":{"enabled":false},"turboThreshold":0,"showInLegend":true},"treemap":{"layoutAlgorithm":"squarified"},"scatter":{"marker":{"symbol":"circle"}}},"series":[{"name":"2seater","data":[{"manufacturer":"chevrolet","model":"corvette","displ":5.7,"year":1999,"cyl":8,"trans":"manual(m6)","drv":"r","cty":16,"hwy":26,"fl":"p","class":"2seater","x":5.7,"y":26},{"manufacturer":"chevrolet","model":"corvette","displ":5.7,"year":1999,"cyl":8,"trans":"auto(l4)","drv":"r","cty":15,"hwy":23,"fl":"p","class":"2seater","x":5.7,"y":23},{"manufacturer":"chevrolet","model":"corvette","displ":6.2,"year":2008,"cyl":8,"trans":"manual(m6)","drv":"r","cty":16,"hwy":26,"fl":"p","class":"2seater","x":6.2,"y":26},{"manufacturer":"chevrolet","model":"corvette","displ":6.2,"year":2008,"cyl":8,"trans":"auto(s6)","drv":"r","cty":15,"hwy":25,"fl":"p","class":"2seater","x":6.2,"y":25},{"manufacturer":"chevrolet","model":"corvette","displ":7,"year":2008,"cyl":8,"trans":"manual(m6)","drv":"r","cty":15,"hwy":24,"fl":"p","class":"2seater","x":7,"y":24}],"type":"scatter"},{"name":"compact","data":[{"manufacturer":"audi","model":"a4","displ":1.8,"year":1999,"cyl":4,"trans":"auto(l5)","drv":"f","cty":18,"hwy":29,"fl":"p","class":"compact","x":1.8,"y":29},{"manufacturer":"audi","model":"a4","displ":1.8,"year":1999,"cyl":4,"trans":"manual(m5)","drv":"f","cty":21,"hwy":29,"fl":"p","class":"compact","x":1.8,"y":29},{"manufacturer":"audi","model":"a4","displ":2,"year":2008,"cyl":4,"trans":"manual(m6)","drv":"f","cty":20,"hwy":31,"fl":"p","class":"compact","x":2,"y":31},{"manufacturer":"audi","model":"a4","displ":2,"year":2008,"cyl":4,"trans":"auto(av)","drv":"f","cty":21,"hwy":30,"fl":"p","class":"compact","x":2,"y":30},{"manufacturer":"audi","model":"a4","displ":2.8,"year":1999,"cyl":6,"trans":"auto(l5)","drv":"f","cty":16,"hwy":26,"fl":"p","class":"compact","x":2.8,"y":26},{"manufacturer":"audi","model":"a4","displ":2.8,"year":1999,"cyl":6,"trans":"manual(m5)","drv":"f","cty":18,"hwy":26,"fl":"p","class":"compact","x":2.8,"y":26},{"manufacturer":"audi","model":"a4","displ":3.1,"year":2008,"cyl":6,"trans":"auto(av)","drv":"f","cty":18,"hwy":27,"fl":"p","class":"compact","x":3.1,"y":27},{"manufacturer":"audi","model":"a4 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IA","value":1.8},{"code":"us-ia-121","name":"Madison County, IA","value":3.6},{"code":"us-ia-123","name":"Mahaska County, IA","value":3.7},{"code":"us-ia-125","name":"Marion County, IA","value":3.2},{"code":"us-ia-127","name":"Marshall County, IA","value":4.7},{"code":"us-ia-129","name":"Mills County, IA","value":3.5},{"code":"us-ia-131","name":"Mitchell County, IA","value":2.7},{"code":"us-ia-133","name":"Monona County, IA","value":4.1},{"code":"us-ia-135","name":"Monroe County, IA","value":3.8},{"code":"us-ia-137","name":"Montgomery County, IA","value":3.3},{"code":"us-ia-139","name":"Muscatine County, IA","value":3.8},{"code":"us-ia-141","name":"O'Brien County, IA","value":2.7},{"code":"us-ia-143","name":"Osceola County, IA","value":2.7},{"code":"us-ia-145","name":"Page County, IA","value":3.8},{"code":"us-ia-147","name":"Palo Alto County, IA","value":3},{"code":"us-ia-149","name":"Plymouth County, IA","value":2.6},{"code":"us-ia-151","name":"Pocahontas County, IA","value":2.7},{"code":"us-ia-153","name":"Polk County, IA","value":3.6},{"code":"us-ia-155","name":"Pottawattamie County, IA","value":3.5},{"code":"us-ia-157","name":"Poweshiek County, IA","value":3.3},{"code":"us-ia-159","name":"Ringgold County, IA","value":2.8},{"code":"us-ia-161","name":"Sac County, IA","value":3.2},{"code":"us-ia-163","name":"Scott County, IA","value":4.6},{"code":"us-ia-165","name":"Shelby County, IA","value":2.6},{"code":"us-ia-167","name":"Sioux County, IA","value":2.2},{"code":"us-ia-169","name":"Story County, IA","value":2.2},{"code":"us-ia-171","name":"Tama County, IA","value":4.4},{"code":"us-ia-173","name":"Taylor County, IA","value":2.9},{"code":"us-ia-175","name":"Union County, IA","value":3.7},{"code":"us-ia-177","name":"Van Buren County, IA","value":3.6},{"code":"us-ia-179","name":"Wapello County, IA","value":4.8},{"code":"us-ia-181","name":"Warren County, IA","value":3.2},{"code":"us-ia-183","name":"Washington County, IA","value":3},{"code":"us-ia-185","name":"Wayne County, IA","value":3.7},{"code":"us-ia-187","name":"Webster County, IA","value":4.3},{"code":"us-ia-189","name":"Winnebago County, IA","value":3.9},{"code":"us-ia-191","name":"Winneshiek County, IA","value":3.3},{"code":"us-ia-193","name":"Woodbury County, IA","value":3.6},{"code":"us-ia-195","name":"Worth County, IA","value":4.3},{"code":"us-ia-197","name":"Wright County, IA","value":3.7},{"code":"us-ks-001","name":"Allen County, KS","value":4.8},{"code":"us-ks-003","name":"Anderson County, KS","value":4.9},{"code":"us-ks-005","name":"Atchison County, KS","value":5.9},{"code":"us-ks-007","name":"Barber County, KS","value":4.4},{"code":"us-ks-009","name":"Barton County, KS","value":4.1},{"code":"us-ks-011","name":"Bourbon County, KS","value":5.1},{"code":"us-ks-013","name":"Brown County, KS","value":4.1},{"code":"us-ks-015","name":"Butler County, KS","value":4.4},{"code":"us-ks-017","name":"Chase County, KS","value":4.2},{"code":"us-ks-019","name":"Chautauqua County, KS","value":6.5},{"code":"us-ks-021","name":"Cherokee County, KS","value":4.7},{"code":"us-ks-023","name":"Cheyenne County, KS","value":2.9},{"code":"us-ks-025","name":"Clark County, KS","value":3.2},{"code":"us-ks-027","name":"Clay County, KS","value":4.2},{"code":"us-ks-029","name":"Cloud County, KS","value":3.7},{"code":"us-ks-031","name":"Coffey County, KS","value":4.9},{"code":"us-ks-033","name":"Comanche County, KS","value":3.6},{"code":"us-ks-035","name":"Cowley County, KS","value":4.5},{"code":"us-ks-037","name":"Crawford County, KS","value":5.3},{"code":"us-ks-039","name":"Decatur County, KS","value":3.8},{"code":"us-ks-041","name":"Dickinson County, KS","value":5.4},{"code":"us-ks-043","name":"Doniphan County, KS","value":4.4},{"code":"us-ks-045","name":"Douglas County, KS","value":3.6},{"code":"us-ks-047","name":"Edwards County, KS","value":3.9},{"code":"us-ks-049","name":"Elk County, KS","value":4.8},{"code":"us-ks-051","name":"Ellis County, KS","value":3.3},{"code":"us-ks-053","name":"Ellsworth County, KS","value":3.6},{"code":"us-ks-055","name":"Finney County, KS","value":3.5},{"code":"us-ks-057","name":"Ford County, KS","value":3.7},{"code":"us-ks-059","name":"Franklin County, KS","value":4.7},{"code":"us-ks-061","name":"Geary County, KS","value":6.2},{"code":"us-ks-063","name":"Gove County, KS","value":2.7},{"code":"us-ks-065","name":"Graham County, KS","value":4.5},{"code":"us-ks-067","name":"Grant County, KS","value":3.8},{"code":"us-ks-069","name":"Gray County, KS","value":2.5},{"code":"us-ks-071","name":"Greeley County, KS","value":2.4},{"code":"us-ks-073","name":"Greenwood County, KS","value":4.9},{"code":"us-ks-075","name":"Hamilton County, KS","value":2.3},{"code":"us-ks-077","name":"Harper County, KS","value":4.5},{"code":"us-ks-079","name":"Harvey County, KS","value":4},{"code":"us-ks-081","name":"Haskell County, KS","value":2.6},{"code":"us-ks-083","name":"Hodgeman County, KS","value":3.6},{"code":"us-ks-085","name":"Jackson County, KS","value":3.8},{"code":"us-ks-087","name":"Jefferson County, KS","value":4.2},{"code":"us-ks-089","name":"Jewell County, KS","value":4.4},{"code":"us-ks-091","name":"Johnson County, KS","value":3.5},{"code":"us-ks-093","name":"Kearny County, KS","value":3},{"code":"us-ks-095","name":"Kingman County, KS","value":4.5},{"code":"us-ks-097","name":"Kiowa County, KS","value":3.5},{"code":"us-ks-099","name":"Labette County, KS","value":6.1},{"code":"us-ks-101","name":"Lane County, KS","value":3.4},{"code":"us-ks-103","name":"Leavenworth County, KS","value":4.6},{"code":"us-ks-105","name":"Lincoln County, KS","value":3.7},{"code":"us-ks-107","name":"Linn County, KS","value":6.1},{"code":"us-ks-109","name":"Logan County, KS","value":2.6},{"code":"us-ks-111","name":"Lyon County, KS","value":4.3},{"code":"us-ks-113","name":"McPherson County, KS","value":3.2},{"code":"us-ks-115","name":"Marion County, KS","value":3.8},{"code":"us-ks-117","name":"Marshall County, KS","value":2.9},{"code":"us-ks-119","name":"Meade County, KS","value":3.2},{"code":"us-ks-121","name":"Miami County, KS","value":4.4},{"code":"us-ks-123","name":"Mitchell County, KS","value":3},{"code":"us-ks-125","name":"Montgomery County, KS","value":6.8},{"code":"us-ks-127","name":"Morris County, KS","value":4.2},{"code":"us-ks-129","name":"Morton County, KS","value":4.7},{"code":"us-ks-131","name":"Nemaha County, KS","value":2.7},{"code":"us-ks-133","name":"Neosho County, KS","value":7.3},{"code":"us-ks-135","name":"Ness County, KS","value":3.5},{"code":"us-ks-137","name":"Norton County, KS","value":2.5},{"code":"us-ks-139","name":"Osage County, KS","value":5},{"code":"us-ks-141","name":"Osborne County, KS","value":3.6},{"code":"us-ks-143","name":"Ottawa County, KS","value":3.6},{"code":"us-ks-145","name":"Pawnee County, KS","value":3.9},{"code":"us-ks-147","name":"Phillips County, KS","value":3.5},{"code":"us-ks-149","name":"Pottawatomie County, KS","value":3.6},{"code":"us-ks-151","name":"Pratt County, KS","value":4.1},{"code":"us-ks-153","name":"Rawlins County, KS","value":2.5},{"code":"us-ks-155","name":"Reno County, KS","value":4.4},{"code":"us-ks-157","name":"Republic County, KS","value":3.2},{"code":"us-ks-159","name":"Rice County, KS","value":3.8},{"code":"us-ks-161","name":"Riley County, KS","value":3.4},{"code":"us-ks-163","name":"Rooks County, KS","value":4.9},{"code":"us-ks-165","name":"Rush County, KS","value":3.7},{"code":"us-ks-167","name":"Russell County, KS","value":4.1},{"code":"us-ks-169","name":"Saline County, KS","value":3.9},{"code":"us-ks-171","name":"Scott County, KS","value":3.3},{"code":"us-ks-173","name":"Sedgwick County, KS","value":5},{"code":"us-ks-175","name":"Seward County, KS","value":4.3},{"code":"us-ks-177","name":"Shawnee County, KS","value":4.3},{"code":"us-ks-179","name":"Sheridan County, KS","value":2.5},{"code":"us-ks-181","name":"Sherman County, KS","value":3.1},{"code":"us-ks-183","name":"Smith County, KS","value":4.7},{"code":"us-ks-185","name":"Stafford County, KS","value":4.3},{"code":"us-ks-187","name":"Stanton County, KS","value":3.5},{"code":"us-ks-189","name":"Stevens County, KS","value":3.9},{"code":"us-ks-191","name":"Sumner County, KS","value":4.6},{"code":"us-ks-193","name":"Thomas County, KS","value":2.8},{"code":"us-ks-195","name":"Trego County, KS","value":4},{"code":"us-ks-197","name":"Wabaunsee County, KS","value":3.7},{"code":"us-ks-199","name":"Wallace County, KS","value":2.7},{"code":"us-ks-201","name":"Washington County, KS","value":3.1},{"code":"us-ks-203","name":"Wichita County, KS","value":3.4},{"code":"us-ks-205","name":"Wilson County, KS","value":6.3},{"code":"us-ks-207","name":"Woodson County, KS","value":4.6},{"code":"us-ks-209","name":"Wyandotte County, KS","value":6},{"code":"us-ky-001","name":"Adair County, KY","value":6.5},{"code":"us-ky-003","name":"Allen County, KY","value":4.5},{"code":"us-ky-005","name":"Anderson County, KY","value":3.9},{"code":"us-ky-007","name":"Ballard County, KY","value":6},{"code":"us-ky-009","name":"Barren County, KY","value":4.6},{"code":"us-ky-011","name":"Bath County, KY","value":6.9},{"code":"us-ky-013","name":"Bell County, KY","value":7.5},{"code":"us-ky-015","name":"Boone County, KY","value":3.8},{"code":"us-ky-017","name":"Bourbon County, KY","value":4.3},{"code":"us-ky-019","name":"Boyd County, KY","value":6.1},{"code":"us-ky-021","name":"Boyle County, KY","value":5.1},{"code":"us-ky-023","name":"Bracken County, KY","value":5.7},{"code":"us-ky-025","name":"Breathitt County, KY","value":8.8},{"code":"us-ky-027","name":"Breckinridge County, KY","value":5.3},{"code":"us-ky-029","name":"Bullitt County, KY","value":4.3},{"code":"us-ky-031","name":"Butler County, KY","value":5.2},{"code":"us-ky-033","name":"Caldwell County, KY","value":5},{"code":"us-ky-035","name":"Calloway County, KY","value":4.3},{"code":"us-ky-037","name":"Campbell County, KY","value":3.9},{"code":"us-ky-039","name":"Carlisle County, KY","value":5},{"code":"us-ky-041","name":"Carroll County, KY","value":5.1},{"code":"us-ky-043","name":"Carter County, KY","value":7.9},{"code":"us-ky-045","name":"Casey County, KY","value":5.5},{"code":"us-ky-047","name":"Christian County, KY","value":5.8},{"code":"us-ky-049","name":"Clark County, KY","value":4.8},{"code":"us-ky-051","name":"Clay County, KY","value":8.6},{"code":"us-ky-053","name":"Clinton County, KY","value":6.8},{"code":"us-ky-055","name":"Crittenden County, KY","value":5.1},{"code":"us-ky-057","name":"Cumberland County, KY","value":5.7},{"code":"us-ky-059","name":"Daviess County, KY","value":4.2},{"code":"us-ky-061","name":"Edmonson County, KY","value":6},{"code":"us-ky-063","name":"Elliott County, 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KY","value":10},{"code":"us-ky-097","name":"Harrison County, KY","value":4.2},{"code":"us-ky-099","name":"Hart County, KY","value":4.8},{"code":"us-ky-101","name":"Henderson County, KY","value":4.5},{"code":"us-ky-103","name":"Henry County, KY","value":4.3},{"code":"us-ky-105","name":"Hickman County, KY","value":5},{"code":"us-ky-107","name":"Hopkins County, KY","value":5.5},{"code":"us-ky-109","name":"Jackson County, KY","value":7.6},{"code":"us-ky-111","name":"Jefferson County, KY","value":4.5},{"code":"us-ky-113","name":"Jessamine County, KY","value":3.9},{"code":"us-ky-115","name":"Johnson County, KY","value":7.4},{"code":"us-ky-117","name":"Kenton County, KY","value":4},{"code":"us-ky-119","name":"Knott County, KY","value":8.7},{"code":"us-ky-121","name":"Knox County, KY","value":7.3},{"code":"us-ky-123","name":"Larue County, KY","value":5},{"code":"us-ky-125","name":"Laurel County, KY","value":5.7},{"code":"us-ky-127","name":"Lawrence County, KY","value":8},{"code":"us-ky-129","name":"Lee County, KY","value":7},{"code":"us-ky-131","name":"Leslie County, KY","value":9.4},{"code":"us-ky-133","name":"Letcher County, KY","value":9.2},{"code":"us-ky-135","name":"Lewis County, KY","value":7.5},{"code":"us-ky-137","name":"Lincoln County, KY","value":6},{"code":"us-ky-139","name":"Livingston County, KY","value":6.4},{"code":"us-ky-141","name":"Logan County, KY","value":4.5},{"code":"us-ky-143","name":"Lyon County, KY","value":5.2},{"code":"us-ky-145","name":"McCracken County, KY","value":5.1},{"code":"us-ky-147","name":"McCreary County, KY","value":7.3},{"code":"us-ky-149","name":"McLean County, KY","value":4.7},{"code":"us-ky-151","name":"Madison County, KY","value":4},{"code":"us-ky-153","name":"Magoffin County, KY","value":12.5},{"code":"us-ky-155","name":"Marion County, KY","value":4.8},{"code":"us-ky-157","name":"Marshall County, KY","value":4.9},{"code":"us-ky-159","name":"Martin County, KY","value":7.3},{"code":"us-ky-161","name":"Mason County, KY","value":5.4},{"code":"us-ky-163","name":"Meade County, KY","value":5.3},{"code":"us-ky-165","name":"Menifee County, KY","value":7.7},{"code":"us-ky-167","name":"Mercer County, KY","value":4.5},{"code":"us-ky-169","name":"Metcalfe County, KY","value":4.6},{"code":"us-ky-171","name":"Monroe County, KY","value":4.3},{"code":"us-ky-173","name":"Montgomery County, KY","value":5.9},{"code":"us-ky-175","name":"Morgan County, KY","value":7.3},{"code":"us-ky-177","name":"Muhlenberg County, KY","value":6},{"code":"us-ky-179","name":"Nelson County, KY","value":4.6},{"code":"us-ky-181","name":"Nicholas County, KY","value":5.1},{"code":"us-ky-183","name":"Ohio County, KY","value":5.5},{"code":"us-ky-185","name":"Oldham County, KY","value":3.6},{"code":"us-ky-187","name":"Owen County, KY","value":3.8},{"code":"us-ky-189","name":"Owsley County, KY","value":8.1},{"code":"us-ky-191","name":"Pendleton County, KY","value":4.7},{"code":"us-ky-193","name":"Perry County, KY","value":7.7},{"code":"us-ky-195","name":"Pike County, KY","value":8},{"code":"us-ky-197","name":"Powell County, KY","value":6.1},{"code":"us-ky-199","name":"Pulaski County, KY","value":5.4},{"code":"us-ky-201","name":"Robertson County, KY","value":5.6},{"code":"us-ky-203","name":"Rockcastle County, KY","value":5.4},{"code":"us-ky-205","name":"Rowan County, KY","value":5.4},{"code":"us-ky-207","name":"Russell County, KY","value":9.3},{"code":"us-ky-209","name":"Scott County, KY","value":3.7},{"code":"us-ky-211","name":"Shelby County, KY","value":3.7},{"code":"us-ky-213","name":"Simpson County, KY","value":4.3},{"code":"us-ky-215","name":"Spencer County, KY","value":3.9},{"code":"us-ky-217","name":"Taylor County, KY","value":5.3},{"code":"us-ky-219","name":"Todd County, KY","value":4.1},{"code":"us-ky-221","name":"Trigg County, KY","value":5},{"code":"us-ky-223","name":"Trimble County, KY","value":5.2},{"code":"us-ky-225","name":"Union County, KY","value":6.1},{"code":"us-ky-227","name":"Warren County, KY","value":4.1},{"code":"us-ky-229","name":"Washington County, KY","value":4},{"code":"us-ky-231","name":"Wayne County, KY","value":7.2},{"code":"us-ky-233","name":"Webster County, KY","value":5},{"code":"us-ky-235","name":"Whitley County, KY","value":6.2},{"code":"us-ky-237","name":"Wolfe County, KY","value":8.3},{"code":"us-ky-239","name":"Woodford County, KY","value":3.4},{"code":"us-la-001","name":"Acadia Parish, LA","value":6.4},{"code":"us-la-003","name":"Allen Parish, LA","value":6.8},{"code":"us-la-005","name":"Ascension Parish, LA","value":5.1},{"code":"us-la-007","name":"Assumption Parish, LA","value":7.6},{"code":"us-la-009","name":"Avoyelles Parish, LA","value":7.3},{"code":"us-la-011","name":"Beauregard Parish, LA","value":6.6},{"code":"us-la-013","name":"Bienville Parish, LA","value":8.6},{"code":"us-la-015","name":"Bossier Parish, 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image","drillUpText":"Back to {series.name}","invalidDate":null,"loading":"Loading...","months":["January","February","March","April","May","June","July","August","September","October","November","December"],"noData":"No data to display","numericSymbols":["k","M","G","T","P","E"],"printChart":"Print chart","resetZoom":"Reset zoom","resetZoomTitle":"Reset zoom level 1:1","shortMonths":["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],"thousandsSep":" ","weekdays":["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]}},"type":"map","fonts":[],"debug":false},"evals":[],"jsHooks":[]} Plus d’informations : http://jkunst.com/highcharter/ Conditions et comparaisons Une condition est une expression logique dont le résultat est soit TRUE (vrai) soit FALSE (faux). Une condition comprend la plupart du temps un opérateur de comparaison . Les plus courants sont les suivants : Opérateur de comparaison Signification == égal à!= différent de > strictement supérieur à < strictement inférieur à >= supérieur ou égal à <= inférieur ou égal à Voyons tout de suite un exemple : library (questionr) data (hdv2003) d <- hdv2003 str (d $ sexe == "Homme") logi [1:2000] FALSE FALSE TRUE TRUE FALSE FALSE ... Que s’est-il passé ? Nous avons fourni à R une condition qui signifie « la valeur de la variable sexe vaut “Homme” ». Et il nous a renvoyé un vecteur avec autant d’éléments qu’il y’a d’observations dans d, et dont la valeur est TRUE si l’observation correspond à un homme et FALSE dans les autres cas. Prenons un autre exemple. On n’affichera cette fois que les premiers éléments de notre variable d’intérêt à l’aide de la fonction head : head (d $ age) [1] 28 23 59 34 71 35 head (d $ age > 40) [1] FALSE FALSE TRUE FALSE TRUE FALSE On voit bien ici qu’à chaque élément du vecteur d$age dont la valeur est supérieure à 40 correspond un élément TRUE dans le résultat de la condition. On peut combiner ou modifier des conditions à l’aide des opérateurs logiques habituels : Opérateur logique Signification & et logique | ou logique! négation logique Comment les utilise-t-on ? Voyons tout de suite des exemples. Supposons que je veuille déterminer quels sont dans mon échantillon les hommes ouvriers spécialisés : d $ sexe == "Homme" & d $ qualif == "Ouvrier specialise" Si je souhaite identifier les personnes qui bricolent ou qui font la cuisine : d $ bricol == "Oui" | d $ cuisine == "Oui" Si je souhaite isoler les femmes qui ont entre 20 et 34 ans : d $ sexe == "Femme" & d $ age >= 20 & d $ age <= 34 Si je souhaite récupérer les enquêtés qui ne sont pas cadres, on peut utiliser l’une des deux formes suivantes : d $ qualif!= "Cadre"! (d $ qualif == "Cadre") Lorsqu’on mélange « et » et « ou » il est nécessaire d’utiliser des parenthèses pour différencier les blocs. La condition suivante identifie les femmes qui sont soit cadre, soit employée : d $ sexe == "Femme" & (d $ qualif == "Employe" | d $ qualif == "Cadre") L’opérateur %in% peut être très utile : il teste si une valeur fait partie des éléments d’un vecteur. Ainsi on pourrait remplacer la condition précédente par : d $ sexe == "Femme" & d $ qualif %in% c ("Employe", "Cadre") Enfin, signalons qu’on peut utiliser les fonctions table ou summary pour avoir une idée du résultat de notre condition : table (d $ sexe) Homme Femme
899 1101 table (d $ sexe == "Homme") FALSE TRUE
1101 899 summary (d $ sexe == "Homme") Mode FALSE TRUE
logical 1101 899 Formules Statistiques descriptives Tableaux croisés avec xtabs Statistiques bivariées avec aggregate Panels graphiques avec lattice Visualisation bivariée Visualisation par petits multiples Spécifier des modèles Les formules R Pour aller plus loin Ce chapitre vise à illustrer l’utilisation de la notation formule de R, qui désigne l’emploi de cette notation par l’expression formula . Cette notation est utilisée par de très nombreuses fonctions de R : on en a notamment vu plusieurs exemples dans le chapitre sur les graphiques bivariés, car l’extension ggplot2 se sert de cette notation dans ses paramètres facet_wrap et facet_grid . Dans ce chapitre, on verra comment se servir de la notation formule dans deux contextes différents. D’une part, on verra que deux fonctions basiques de R se servent de cette notation pour produire des tableaux croisés et des statistiques bivariées. D’autre part, on verra que l’extension lattice se sert de cette notation pour créer des graphiques panelisés, dits graphiques à petits multiples . Dans plusieurs autres chapitres, les opérations décrites ci-dessus sont effectuées avec les extensions dplyr d’une part, et ggplot2 d’autre part. On se servira également de ces extensions dans ce chapitre, de manière à mener une comparaison des différentes manières d’effectuer certaines opérations dans R, avec ou sans la notation formule : library (dplyr) library (ggplot2) Statistiques descriptives Les premiers exemples de ce chapitre montrent l’utilisation de cette notation pour produire des tableaux croisés et des statistiques descriptives. Le jeu de données utilisé, hdv2003, a déjà été utilisé dans plusieurs chapitres, et font partie de l’extension questionr . Chargeons cette extension et le jeu de données hdv2003 : library (questionr) data (hdv2003) Pour rappel, ce jeu de données contient des individus, leur âge, leur statut professionnel, et le nombre d’heures quotidiennes passées à regarder la télévision. glimpse (hdv2003, 75) Rows: 2,000
Columns: 20
$ id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1...
$ age <int> 28, 23, 59, 34, 71, 35, 60, 47, 20, 28, 65, 47, ...
$ sexe <fct> Femme, Femme, Homme, Homme, Femme, Femme, Femme,...
$ nivetud <fct> "Enseignement superieur y compris technique supe...
$ poids <dbl> 2634.3982, 9738.3958, 3994.1025, 5731.6615, 4329...
$ occup <fct> "Exerce une profession", "Etudiant, eleve", "Exe...
$ qualif <fct> Employe, NA, Technicien, Technicien, Employe, Em...
$ freres.soeurs <int> 8, 2, 2, 1, 0, 5, 1, 5, 4, 2, 3, 4, 1, 5, 2, 3, ...
$ clso <fct> Oui, Oui, Non, Non, Oui, Non, Oui, Non, Oui, Non...
$ relig <fct> Ni croyance ni appartenance, Ni croyance ni appa...
$ trav.imp <fct> Peu important, NA, Aussi important que le reste,...
$ trav.satisf <fct> Insatisfaction, NA, Equilibre, Satisfaction, NA,...
$ hard.rock <fct> Non, Non, Non, Non, Non, Non, Non, Non, Non, Non...
$ lecture.bd <fct> Non, Non, Non, Non, Non, Non, Non, Non, Non, Non...
$ peche.chasse <fct> Non, Non, Non, Non, Non, Non, Oui, Oui, Non, Non...
$ cuisine <fct> Oui, Non, Non, Oui, Non, Non, Oui, Oui, Non, Non...
$ bricol <fct> Non, Non, Non, Oui, Non, Non, Non, Oui, Non, Non...
$ cinema <fct> Non, Oui, Non, Oui, Non, Oui, Non, Non, Oui, Oui...
$ sport <fct> Non, Oui, Oui, Oui, Non, Oui, Non, Non, Non, Oui...
$ heures.tv <dbl> 0.0, 1.0, 0.0, 2.0, 3.0, 2.0, 2.9, 1.0, 2.0, 2.0... Tableaux croisés avec xtabs Utilisons, pour ce premier exemple, la variable occup du jeu de données hdv2003, qui correspond au statut professionnel des individus inclus dans l’échantillon. La fonction de base pour compter les individus par statut est la fonction table : table (hdv2003 $ occup) Exerce une profession Chomeur
1049 134
Etudiant, eleve Retraite
94 392
Retire des affaires Au foyer
77 171
Autre inactif
83 Avec la fonction xtabs, le même résultat est produit à partir de la notation suivante : xtabs (~ occup, data = hdv2003) occup
Exerce une profession Chomeur
1049 134
Etudiant, eleve Retraite
94 392
Retire des affaires Au foyer
77 171
Autre inactif
83 Le premier argument est une formule, au sens où R entend cette expression. Le second argument, data, correspond au jeu de données auquel la formule doit être appliquée. On pourra se passer d’écrire explicitement cet argument dans les exemples suivants. L’avantage de la fonction xtabs n’est pas évident dans ce premier exemple. En réalité, cette fonction devient utile lorsque l’on souhaite construire un ou plusieurs tableau(x) croisé(s). Par exemple, pour croiser la variable occup avec la variable sexe, une solution constiste à écrire : with (hdv2003, table (occup, sexe)) sexe
occup Homme Femme
Exerce une profession 520 529
Chomeur 54 80
Etudiant, eleve 48 46
Retraite 208 184
Retire des affaires 39 38
Au foyer 0 171
Autre inactif 30 53 Ou alors, ce qui revient au même : table (hdv2003 $ occup, hdv2003 $ sexe) Avec xtabs, la même opération s’écrit de la manière suivante : xtabs (~ occup + sexe, hdv2003) sexe
occup Homme Femme
Exerce une profession 520 529
Chomeur 54 80
Etudiant, eleve 48 46
Retraite 208 184
Retire des affaires 39 38
Au foyer 0 171
Autre inactif 30 53 Cette écriture est plus courte que le code équivalent dans dplyr : group_by (hdv2003, occup) %>% summarise (Homme = sum (sexe == "Homme"), Femme = sum (sexe == "Femme")) {"columns":[{"label":["Homme"],"name":[1],"type":["int"],"align":["right"]},{"label":["Femme"],"name":[2],"type":["int"],"align":["right"]}],"data":[{"1":"899","2":"1101"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} De plus, xtabs permet de créer plusieurs tableaux croisés en une seule formule : xtabs (~ occup + sexe + trav.imp, hdv2003), , trav.imp = Le plus important

sexe
occup Homme Femme
Exerce une profession 13 16
Chomeur 0 0
Etudiant, eleve 0 0
Retraite 0 0
Retire des affaires 0 0
Au foyer 0 0
Autre inactif 0 0

, , trav.imp = Aussi important que le reste

sexe
occup Homme Femme
Exerce une profession 159 100
Chomeur 0 0
Etudiant, eleve 0 0
Retraite 0 0
Retire des affaires 0 0
Au foyer 0 0
Autre inactif 0 0

, , trav.imp = Moins important que le reste

sexe
occup Homme Femme
Exerce une profession 328 380
Chomeur 0 0
Etudiant, eleve 0 0
Retraite 0 0
Retire des affaires 0 0
Au foyer 0 0
Autre inactif 0 0

, , trav.imp = Peu important

sexe
occup Homme Femme
Exerce une profession 20 32
Chomeur 0 0
Etudiant, eleve 0 0
Retraite 0 0
Retire des affaires 0 0
Au foyer 0 0
Autre inactif 0 0 Cet exemple permet simplement de réaliser que la variable trav.imp, qui contient les réponses à une question portant sur l’importance du travail, n’a été mesurée (c’est-à-dire que la question n’a été posée) qu’aux seuls individus actifs de l’échantillon. Statistiques bivariées avec aggregate aggregate (heures.tv ~ sexe, mean, data = hdv2003) {"columns":[{"label":["sexe"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["heures.tv"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"Homme","2":"2.219330"},{"1":"Femme","2":"2.268727"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Ici, le premier argument est à nouveau une formule. Le second argument correspond à la statistique descriptive que l’on souhaite obtenir, et le dernier argument indique le jeu de données auquel appliquer les deux autres arguments. On peut d’ailleurs obtenir le même résultat en respectant de manière plus stricte l’ordre des arguments dans la syntaxe de la fonction aggregate : aggregate (heures.tv ~ sexe, hdv2003, mean) {"columns":[{"label":["sexe"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["heures.tv"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"Homme","2":"2.219330"},{"1":"Femme","2":"2.268727"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Cette écriture est, à nouveau, plus compacte que le code équivalent dans dplyr, qui demande de spécifier le retrait des valeurs manquantes : group_by (hdv2003, sexe) %>% summarise (heures.tv = mean (heures.tv, na.rm = TRUE)) À nouveau, on va pouvoir combiner plusieurs variables dans la formule que l’on passe à aggregate, ce qui va permettre d’obtenir la moyenne des heures de télévision quotidiennes par sexe et par statut professionnel : aggregate (heures.tv ~ sexe + occup, hdv2003, mean) {"columns":[{"label":["sexe"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["occup"],"name":[2],"type":["fctr"],"align":["left"]},{"label":["heures.tv"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"Homme","2":"Exerce une profession","3":"1.920463"},{"1":"Femme","2":"Exerce une profession","3":"1.724953"},{"1":"Homme","2":"Chomeur","3":"2.853846"},{"1":"Femme","2":"Chomeur","3":"2.888608"},{"1":"Homme","2":"Etudiant, eleve","3":"1.400000"},{"1":"Femme","2":"Etudiant, eleve","3":"1.256522"},{"1":"Homme","2":"Retraite","3":"2.826442"},{"1":"Femme","2":"Retraite","3":"2.877174"},{"1":"Homme","2":"Retire des affaires","3":"2.410256"},{"1":"Femme","2":"Retire des affaires","3":"2.844737"},{"1":"Femme","2":"Au foyer","3":"2.822222"},{"1":"Homme","2":"Autre inactif","3":"3.133333"},{"1":"Femme","2":"Autre inactif","3":"3.339623"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} La même opération demanderait toujours un peu plus de code avec dplyr : group_by (hdv2003, occup, sexe) %>% summarise (heures.tv = mean (heures.tv, na.rm = TRUE)) La fonction aggregate permet bien sûr d’utiliser une autre fonction que la moyenne, comme dans cet exemple, suivi de son équivalent avec dplyr : # âge médian par sexe et statut professionnel aggregate (age ~ sexe + occup, hdv2003, median) {"columns":[{"label":["sexe"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["occup"],"name":[2],"type":["fctr"],"align":["left"]},{"label":["age"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"Homme","2":"Exerce une profession","3":"42.0"},{"1":"Femme","2":"Exerce une profession","3":"41.0"},{"1":"Homme","2":"Chomeur","3":"35.5"},{"1":"Femme","2":"Chomeur","3":"40.0"},{"1":"Homme","2":"Etudiant, eleve","3":"20.0"},{"1":"Femme","2":"Etudiant, eleve","3":"20.5"},{"1":"Homme","2":"Retraite","3":"68.0"},{"1":"Femme","2":"Retraite","3":"69.0"},{"1":"Homme","2":"Retire des affaires","3":"70.0"},{"1":"Femme","2":"Retire des affaires","3":"74.0"},{"1":"Femme","2":"Au foyer","3":"51.0"},{"1":"Homme","2":"Autre inactif","3":"56.0"},{"1":"Femme","2":"Autre inactif","3":"58.0"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # code équivalent avec l'extension 'dplyr' group_by (hdv2003, occup, sexe) %>% summarise (age = median (age, na.rm = TRUE)) Si, comme dans le cas de summarise, on souhaite passer des arguments supplémentaires à la fonction median, il suffit de les lister à la suite du nom de la fonction. Par exemple, on écrirait : aggregate(age ~ sexe + occup, hdv2003, median, na.rm = TRUE) . Ceci étant, aggregate utilise par défaut l’option na.action = na.omit, donc il est bon de se rappeler que l’on peut désactiver cette option en utilisant l’option na.action = na.pass, ce qui permet éventuellement de conserver des lignes vides dans le tableau de résultat. La fonction aggregate permet, par ailleurs, d’obtenir des résultats à plusieurs colonnes. Dans l’exemple ci-dessus, on illustre ce principe avec la fonction range, qui renvoie deux résultats (la valeur minimale et la valeur maximale de la variable, qui est toujours la variable age), chacun présentés dans une colonne : aggregate (age ~ sexe + occup, hdv2003, range) sexe occup age.1 age.2
1 Homme Exerce une profession 18 63
2 Femme Exerce une profession 18 67
3 Homme Chomeur 18 63
4 Femme Chomeur 18 63
5 Homme Etudiant, eleve 18 34
6 Femme Etudiant, eleve 18 35
7 Homme Retraite 48 92
8 Femme Retraite 41 96
9 Homme Retire des affaires 57 91
10 Femme Retire des affaires 57 93
11 Femme Au foyer 22 90
12 Homme Autre inactif 39 71
13 Femme Autre inactif 19 97 Cette fonction ne peut pas être facilement écrite dans dplyr sans réécrire chacune des colonnes, ce que le bloc de code suivant illustre. On y gagne en lisibilité dans les intitulés de colonnes : group_by (hdv2003, occup, sexe) %>% summarise (min = min (age, na.rm = TRUE), max = max (age, na.rm = TRUE)) On pourrait de même définir sa propre fonction et la passer à aggregate : f <- function (x) c (mean = mean (x, na.rm = TRUE), sd = sd (x, na.rm = TRUE)) aggregate (age ~ sexe + occup, hdv2003, f) sexe occup age.mean age.sd
1 Homme Exerce une profession 41.461538 10.438113
2 Femme Exerce une profession 40.710775 10.203864
3 Homme Chomeur 38.925926 13.256329
4 Femme Chomeur 38.012500 11.648321
5 Homme Etudiant, eleve 20.895833 2.926326
6 Femme Etudiant, eleve 21.586957 3.249452
7 Homme Retraite 68.418269 8.018882
8 Femme Retraite 69.510870 8.228957
9 Homme Retire des affaires 71.179487 7.687556
10 Femme Retire des affaires 73.789474 7.651737
11 Femme Au foyer 50.730994 15.458412
12 Homme Autre inactif 54.166667 6.597196
13 Femme Autre inactif 59.962264 14.660206 Mais on réalisera vite une des limitations de aggregate dans ce cas-là : le tableau retourné ne contient pas 4 colonnes, mais 3 uniquement, ce que l’on peut vérifier à l’aide de dim ou str . str (aggregate (age ~ sexe + occup, hdv2003, f))'data.frame': 13 obs. of 3 variables:
$ sexe : Factor w/ 2 levels "Homme","Femme": 1 2 1 2 1 2 1 2 1 2 ...
$ occup: Factor w/ 7 levels "Exerce une profession",..: 1 1 2 2 3 3 4 4 5 5 ...
$ age : num [1:13, 1:2] 41.5 40.7 38.9 38 20.9 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:2] "mean" "sd" Pour ce type d’opération, dans lequel on souhaite récupérer plusieurs variables calculées afin de travailler sur ces données agrégées soit dans le cadre d’opérations numériques soit de constructions graphiques, dplyr ou Hmisc s’avèrent plus commodes. Voici un exemple avec summarize de l’extension Hmisc : library (Hmisc, quietly = TRUE) Attaching package: 'Hmisc' The following object is masked from 'package:gt':

html The following object is masked from 'package:survey':

deff The following object is masked from 'package:ggdendro':

label The following objects are masked from 'package:plyr':

is.discrete, summarize The following objects are masked from 'package:dplyr':

src, summarize The following objects are masked from 'package:questionr':

describe, wtd.mean, wtd.table, wtd.var The following objects are masked from 'package:base':

format.pval, units with (hdv2003, summarize (age, llist (sexe, occup), f)) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["sexe"],"name":[1],"type":["S3: labelled"],"align":["right"]},{"label":["occup"],"name":[2],"type":["S3: labelled"],"align":["right"]},{"label":["age"],"name":[3],"type":["S3: labelled"],"align":["right"]},{"label":["sd"],"name":[4],"type":["S3: labelled"],"align":["right"]}],"data":[{"1":"Homme","2":"Exerce une profession","3":"41.46154","4":"10.438113","_rn_":"11"},{"1":"Homme","2":"Chomeur","3":"38.92593","4":"13.256329","_rn_":"9"},{"1":"Homme","2":"Etudiant, eleve","3":"20.89583","4":"2.926326","_rn_":"10"},{"1":"Homme","2":"Retraite","3":"68.41827","4":"8.018882","_rn_":"13"},{"1":"Homme","2":"Retire des affaires","3":"71.17949","4":"7.687556","_rn_":"12"},{"1":"Homme","2":"Autre inactif","3":"54.16667","4":"6.597196","_rn_":"8"},{"1":"Femme","2":"Exerce une profession","3":"40.71078","4":"10.203864","_rn_":"5"},{"1":"Femme","2":"Chomeur","3":"38.01250","4":"11.648321","_rn_":"3"},{"1":"Femme","2":"Etudiant, eleve","3":"21.58696","4":"3.249452","_rn_":"4"},{"1":"Femme","2":"Retraite","3":"69.51087","4":"8.228957","_rn_":"7"},{"1":"Femme","2":"Retire des affaires","3":"73.78947","4":"7.651737","_rn_":"6"},{"1":"Femme","2":"Au foyer","3":"50.73099","4":"15.458412","_rn_":"1"},{"1":"Femme","2":"Autre inactif","3":"59.96226","4":"14.660206","_rn_":"2"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Notons que Hmisc offre déjà une telle fonction (smean.sd), ce qui nous aurait épargné d’écrire notre propre fonction, f, et il en existe bien d’autres. Voici un exemple avec des intervalles de confiance estimés par bootstrap : with (hdv2003, summarize (age, llist (sexe, occup), smean.cl.boot)) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["sexe"],"name":[1],"type":["S3: labelled"],"align":["right"]},{"label":["occup"],"name":[2],"type":["S3: labelled"],"align":["right"]},{"label":["age"],"name":[3],"type":["S3: labelled"],"align":["right"]},{"label":["Lower"],"name":[4],"type":["S3: labelled"],"align":["right"]},{"label":["Upper"],"name":[5],"type":["S3: labelled"],"align":["right"]}],"data":[{"1":"Homme","2":"Exerce une profession","3":"41.46154","4":"40.45952","5":"42.35615","_rn_":"11"},{"1":"Homme","2":"Chomeur","3":"38.92593","4":"35.59213","5":"42.26019","_rn_":"9"},{"1":"Homme","2":"Etudiant, eleve","3":"20.89583","4":"20.16667","5":"21.87500","_rn_":"10"},{"1":"Homme","2":"Retraite","3":"68.41827","4":"67.33113","5":"69.56286","_rn_":"13"},{"1":"Homme","2":"Retire des affaires","3":"71.17949","4":"69.07692","5":"73.53846","_rn_":"12"},{"1":"Homme","2":"Autre inactif","3":"54.16667","4":"51.86583","5":"56.46750","_rn_":"8"},{"1":"Femme","2":"Exerce une profession","3":"40.71078","4":"39.84877","5":"41.64650","_rn_":"5"},{"1":"Femme","2":"Chomeur","3":"38.01250","4":"35.26250","5":"40.81250","_rn_":"3"},{"1":"Femme","2":"Etudiant, eleve","3":"21.58696","4":"20.73913","5":"22.52228","_rn_":"4"},{"1":"Femme","2":"Retraite","3":"69.51087","4":"68.25516","5":"70.71223","_rn_":"7"},{"1":"Femme","2":"Retire des affaires","3":"73.78947","4":"71.31579","5":"75.92171","_rn_":"6"},{"1":"Femme","2":"Au foyer","3":"50.73099","4":"48.30950","5":"53.09942","_rn_":"1"},{"1":"Femme","2":"Autre inactif","3":"59.96226","4":"55.99953","5":"63.83066","_rn_":"2"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Enfin, il est également possible d’utiliser plusieurs variables numériques à gauche de l’opérateur ~ . En voici une illustration : aggregate (cbind (age, poids) ~ sexe + occup, hdv2003, mean) {"columns":[{"label":["sexe"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["occup"],"name":[2],"type":["fctr"],"align":["left"]},{"label":["age"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["poids"],"name":[4],"type":["dbl"],"align":["right"]}],"data":[{"1":"Homme","2":"Exerce une profession","3":"41.46154","4":"5989.928"},{"1":"Femme","2":"Exerce une profession","3":"40.71078","4":"5186.020"},{"1":"Homme","2":"Chomeur","3":"38.92593","4":"4334.421"},{"1":"Femme","2":"Chomeur","3":"38.01250","4":"4168.980"},{"1":"Homme","2":"Etudiant, eleve","3":"20.89583","4":"8712.073"},{"1":"Femme","2":"Etudiant, eleve","3":"21.58696","4":"6809.416"},{"1":"Homme","2":"Retraite","3":"68.41827","4":"4459.116"},{"1":"Femme","2":"Retraite","3":"69.51087","4":"5195.785"},{"1":"Homme","2":"Retire des affaires","3":"71.17949","4":"8786.239"},{"1":"Femme","2":"Retire des affaires","3":"73.78947","4":"10314.848"},{"1":"Femme","2":"Au foyer","3":"50.73099","4":"5471.705"},{"1":"Homme","2":"Autre inactif","3":"54.16667","4":"3740.738"},{"1":"Femme","2":"Autre inactif","3":"59.96226","4":"4679.955"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} Panels graphiques avec lattice Les exemples suivants montreront ensuite comment la notation formule peut servir à produire des graphiques par panel avec l’extension lattice . library (lattice) L’extension lattice présente l’avantage d’être installée par défaut avec R . Il n’est donc pas nécessaire de l’installer préalablement. Chargeons les mêmes données que le chapitre sur les graphiques bivariés . # charger l'extension lisant le format CSV library (readr) # emplacement souhaité pour le jeu de données file = "data/debt.csv" # télécharger le jeu de données s'il n'existe pas if (! file.exists (file)) download.file ("http://www.stat.cmu.edu/~cshalizi/uADA/13/hw/11/debt.csv", file, mode = "wb") # charger les données dans l'objet 'debt' debt = read_csv (file) Warning: Missing column names filled in: 'X1' [1] Parsed with column specification:
cols(
X1 = col_double(),
Country = col_character(),
Year = col_double(),
growth = col_double(),
ratio = col_double()
) Rejetons rapidement un coup d’oeil à ces données, qui sont structurées par pays (variable Country) et par année (variable Year). On y trouve deux variables, growth (le taux de croissance du produit intérieur brut réel), et ratio (le ratio entre la dette publique et le produit intérieur brut), ainsi qu’une première colonne vide, ne contenant que des numéros lignes, dont on va se débarrasser : # inspection des données glimpse (debt, 75) Rows: 1,171
Columns: 5
$ X1 <dbl> 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,...
$ Country <chr> "Australia", "Australia", "Australia", "Australia", "A...
$ Year <dbl> 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, ...
$ growth <dbl> -3.5579515, 2.4594746, 6.4375341, 6.6119938, 6.9202012...
$ ratio <dbl> 190.41908, 177.32137, 148.92981, 125.82870, 109.80940,... # suppression de la première colonne debt = debt[, -1] Visualisation bivariée Le même graphique s’écrit de la manière suivante avec l’extension lattice : xyplot (growth ~ Year, data = debt) Visualisation par petits multiples Appliquons désormais la même visualisation par petits multiples que vue dans le chapitre : xyplot (growth ~ Year | Country, data = debt) Enfin, rajoutons quelques options au graphique, afin de montrer comment l’extension lattice fonctionne : xyplot (growth ~ Year | Country, type = c ("o", "l"), main = "Données Reinhart et Rogoff corrigées, 1946-2009", ylab = "Taux de croissance du PIB", xlab = NULL, data = debt) Spécifier des modèles Les formules R En réalité, la notation par formule qu’utilise R est celle proposée par Wilkinson et al. dans les années 70 pour schématiser la relation entre plusieurs variables dans un plan d’expérience. Plus spécifiquement, l’idée revient à exprimer une relation fonctionnelle, symbolise´e par l’opérateur ~, entre une variable re´ponse y et une ou plusieurs variables explicatives. Disons, pour simplifier, que y est une variable d’intérêt (numérique ou facteur selon le type de modèle), x une variable numérique et que a et b sont des variables catégorielles (des facteurs dans le langage R). Voici les principales relations auxquelles on peut s’intéresser dans un modèle statistique : y ~ x : régression simple, y ~ x + 0 : idem avec suppression du terme d’ordonnée à l’origine, y ~ a + b : régresse avec deux effets principaux indépendants, y ~ a * b : idem avec interaction (équivalent à 1 + a + b + a:b), y ~ a / b : idem en considérant une relation d’emboîtement (équivalent à 1 + a + b + a %in% b). L’opérateur | est quant à lui utilisé par l’extension lme4 dans le cadre de modèles mixtes avec effets aléatoires. Voir le chapitre dédié à la régression logistique pour des exemples de modèles multivariés et le chapitre dédié aux effets d’interaction pour plus de détails sur cette notion. Pour aller plus loin Comme vient de le voir dans ce chapitre, la notation formule apparaît çà et là dans les différentes fonctions de R est de ses extensions. Il est par conséquent utile d’en connaître les rudiments, et en particulier les opérateurs ~ (tilde) et +, ne serait-ce que pour pouvoir se servir des différentes fonctions présentées sur cette page. Le chapitre lattice et les formules fournit plus de détails sur ces aspects. La notation formule devient cruciale dès que l’on souhaite rédiger des modèles : la formule y ~ x, par exemple, qui est équivalente à la formule y ~ 1 + x, correspond à l’équation mathématique Y = a + b X . On trouvera de nombreux exemples d’usage de cette notation dans les chapitres consacrés, notamment, à la régression linéaire ou à la régression logistique . De la même manière, l’opérateur | (pipe) utilisé par l’extension lattice joue aussi un rôle très important dans la rédaction de modèles multi-niveaux, où il sert à indiquer les variables à pentes ou à coefficients aléatoires. Ces modèles sont présentés dans un chapitre dédié . Structures conditionnelles Les boucles avec while Les boucles avec for Les conditions Les instructions if \dots else La fonction switch L’instruction repeat \dots break L’instruction next Barre de progression La version originale de ce chapitre a été écrite par Ewen Gallic dans le cadre de son support de cours d’Ewen Gallic intitulé Logiciel R et programmation, chapitre 4 Boucles et calculs vectoriels . Il existe deux sortes de boucles dans R . Celles pour lesquelles les itérations continuent tant qu’une condition n’est pas invalidée (while), et celles pour lesquelles le nombre d’itérations est défini au moment de lancer la boucle (for). Avant de présenter chacune de ces fonctions, il est nécessaire de préciser que les boucles ne sont pas le point fort de R . Dès que l’on souhaite appliquer une fonction à chaque élément d’un vecteur, et/ou que le résultat de chaque itération ne dépend pas de l’itération précédente, il est préférable de vectoriser les calculs (voir le chapitre sur la vectorisation). Les boucles avec while Quand on souhaite répéter un calcul tant qu’une condition est satisfaite, on utilise la fonction while, avec la syntaxte suivante : while (condition) {instruction} avec condition une valeur logique (TRUE ou FALSE), et instruction du code, qui peut être entouré d’accolades si on souhaite évaluer plusieurs instructions. Le code instruction sera répété tant que condition est vrai. Prenons l’exemple d’une plante qui mesure 10 centimètres et qui va grandir de 10 % tous les ans jusqu’à atteindre 2 mètres. taille <- 0.1 duree <- 0 while (taille < 2) {taille <- taille * 1.1 duree <- duree + 1} message (glue :: glue ("La plante a atteint {round(taille, 1)} mètres en {duree} années.")) La plante a atteint 2.1 mètres en 32 années. Les boucles avec for Quand on connaît le nombre d’itérations à l’avance, on peut utiliser la boucle for . La syntaxe est la suivante : for (variable in vector) {instruction} avec variable le nom d’une variable locale à la boucle for, vector un vecteur à n éléments définissant les valeurs que prendra variable pour chacun des n tours, et instruction le code à exécuter à chaque itération. On peut utiliser for pour remplir les éléments d’une liste, ou d’un vecteur. # Mauvaise manière resultat <- NULL for (i in 1 : 3) {resultat[i] <- i} resultat [1] 1 2 3 À chaque itération, R doit trouver le vecteur de destination en mémoire, créer un nouveau vecteur qui permettra de contenir plus de données, copier données depuis l’ancien vecteur pour les insérer dans le nouveau, et enfin supprimer l’ancien vecteur (Ross, 2014). C’est une opération coûteuse en temps. Un moyen de rendre cette allocation plus efficace est de créer a priori le vecteur ou la liste en le remplissant avec des données manquantes. Ainsi, R n’aura pas besoin de ré-allouer la mémoire à chaque itération. # Manière plus économique resultat <- rep (NA, 3) for (i in 1 : 3) {resultat[i] <- i} resultat [1] 1 2 3 Les conditions On peut soumettre l’exécution de codes en R à conditions que certaines conditions soient honorées. Les instructions if \dots else Les instructions if et else fournissent un moyen d’exécuter du code si une condition est respectée ou non. La syntaxe prend deux formes : # Première forme (pas de code si condition == FALSE) if (condition) {instruction si vrai} # Seconde forme if (condition) {instruction si vrai} else {instruction si faux} avec condition un logique, instruction si vrai le code à exécuter si la condition est vérifiée et instruction si faux le code à exécuter si la condition n’est pas remplie. À nouveau, on peut avoir recours aux accolades pour créer des regroupements. # Simple condition x <- 2 if (x == 2) print ("Hello") [1] "Hello" x <- 3 if (x == 2) print ("Hello") # Avec des instructions dans le cas contraire if (x == 2) print ("Hello") else print ("x est différent de 2") [1] "x est différent de 2" if (x == 2) {print ("Hello")} else {x <- x - 1 print (paste0 ("La nouvelle valeur de x : ", x))} [1] "La nouvelle valeur de x : 2" Attention, lorsque l’on fait des regroupements et qu’on utilise la structure if et else, il est nécessaire d’écrire le mot else sur la même ligne que la parenthèse fermante du groupe d’instructions à réaliser dans le cas où la condition du if est vérifiée. La fonction switch Avec la fonction switch, on peut indiquer à R d’exécuter un code en fonction du résultat obtenu lors d’un test. La syntaxe est la suivante : switch (valeur_test, cas_1 = {instruction_cas_1}, cas_2 = {instruction_cas_2}, ...) avec valeur_test un nombre ou une chaîne de caractères. Si valeur_test vaut cas_1, alors uniquement instruction_cas_1 sera évaluée, si valeur_test vaut cas_2, alors ce sera instruction_cas_2 qui le sera, et ainsi de suite. On peut rajouter une valeur par défaut en utilisant la syntaxte suivante : switch (valeur_test, cas_1 = {instruction_cas_1}, cas_2 = {instruction_cas_2}, ..., {instruction_defaut}) Voici un exemple d’utilisation, issu de la page d’aide de la fonction. centre <- function (x, type) {switch (type, mean = mean (x), median = median (x), trimmed = mean (x, trim = 0.1))} x <- rcauchy (10) centre (x, "mean") [1] -0.488771 centre (x, "median") [1] -0.09931128 centre (x, "trimmed") [1] -0.1281635 L’instruction repeat \dots break L’instruction repeat permet de répéter une expression. Il est nécessaire d’ajouter un test d’arrêt, à l’aide de l’instruction break . i <- 1 repeat {i <- i + 1 if (i == 3) break} i [1] 3 L’instruction next L’instruction next autorise de passer immédiatement à l’itération suivante d’une boucle for, while ou repeat . result <- rep (NA, 10) for (i in 1 : 10) {if (i == 5) next result[i] <- i} # Le 5e élément de result n'a pas été traité result [1] 1 2 3 4 NA 6 7 8 9 10 Barre de progression Lorsque l’exécution d’une boucle prend du temps, il peut être intéressant d’avoir une idée de l’état d’avancement des itérations. Pour cela, il est bien sûr possible d’afficher une valeur dans la console à chaque tour, chaque 10 tours, etc. La fonction txtProgressBar de l’extension utils permet un affichage d’une barre de progression dans la console. Il suffit de lui fournir une valeur minimale et maximale, et de la mettre à jour à chaque itération. Le paramètre style autorise de surcroit à choisir un style pour la barre. Le style numéro 3 affiche un pourcentage de progression, et est utile lorsque d’autres résultats sont affichés dans la console lors de l’exécution de la boucle, dans la mesure où la barre est de nouveau affichée au complet dans la console si nécessaire. Dans l’exemple qui suit, à chacun des dix tours, une pause de 0.1 seconde est effectuée, puis la barre de progression est mise à jour. nb_tours <- 10 pb <- txtProgressBar (min = 1, max = nb_tours, style = 3) for (i in 1 : nb_tours) {Sys.sleep (0.1) setTxtProgressBar (pb, i)} |
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|==================================================| 100% Pour plus d’options, on pourra se référer à la fonction progress_bar de l’extension progress, présentée en détail sur https://r-pkg.org/pkg/progress . Si l’exécution est vraiment longue, et qu’on est impatient de connaître les résultats, il existe de plus une fonction amusante dans l’extension beepr, qui porte le nom de beep . Plusieurs sons peuvent être utilisés (voir la page d’aide de la fonction). library (beepr) beep ("mario") Vectorisation Les fonctions de l’extension plyr Array en input : a*ply Data.frame en input : d*ply List en input : l*ply Calcul parallèle Les fonctions de la famille apply du package base La fonction lapply La fonction sapply La fonction vapply La fonction apply La fonction tapply La fonction mapply La fonction Vectorize La version originale de ce chapitre a été écrite par Ewen Gallic dans le cadre de son support de cours d’Ewen Gallic intitulé Logiciel R et programmation, chapitre 4 Boucles et calculs vectoriels . Les boucles sont des opérations lentes en R . Il est cependant possible, dans de nombreux cas, d’éviter de les employer, en ayant recours à la vectorisation : au lieu d’appliquer une fonction à un scalaire, on l’applique à un vecteur. En fait, nous avons déjà eu recours à maintes reprises aux calculs vectoriels. En effet, lorsque nous avons procédé à des additions, des multiplications, etc. sur des vecteurs, nous avons effectué des calculs vectoriels. Empruntons un exemple à Burns (2011) : dans des langages comme le C, pour effectuer la somme des logarithmes naturels des n premiers entiers, voici une manière de faire : # Somme des logarithmes des 10 premiers entiers somme_log <- 0 for (i in seq_len (10)) {somme_log <- somme_log + log (i)} somme_log [1] 15.10441 Il est possible d’obtenir le même résultat, à la fois d’une manière plus élégante, mais surtout plus efficace en vectorisant le calcul : sum (log (seq_len (10))) [1] 15.10441 Derrière ce code, la fonction log applique la fonction logarithme sur toutes les valeurs du vecteur donné en paramètre. La fonction sum, quant à elle, se charge d’additionner tous les éléments du vecteur qui lui est donné en paramètre. Ces deux fonctions utilisent la vectorisation, mais d’une manière différente : la fonction log applique une opération à chaque élément d’un vecteur, tandis que la fonction sum produit un résultat basé sur l’ensemble du vecteur. L’avantage d’utiliser des fonctions vectorielles plutôt que d’écrire une boucle pour effectuer le calcul, est que ces premières font appel à des fonctions rédigées en C ou FORTRAN, qui utilisent aussi des boucles, mais comme ce sont des langages compilés et non pas interprétés, les itérations sont réalisées dans un temps réduit. Il existe des fonctions, rédigées en C qui effectuent des boucles for . On leur donne souvent le nom de “fonctions de la famille apply”. Il ne s’agit pas de la vectorisation, mais ces fonctions sont souvent mentionnées dès que l’on parle de ce sujet. Ce sont des fonctionnelles qui prennent une fonction en input et retournent un vecteur en output (Wickham, 2014). Ces fonctions sont très utilisées, mais elles souffrent d’un manque d’uniformité. En effet, elles ont été rédigées par des personnes différentes, ayant chacune leur convention. L’extension plyr remédie à ce problème, et ajoute par la même occasion des fonctions supplémentaires, pour couvrir plus de cas que les “fonctions de la famille apply”. Nous allons donc présenter dans un premier temps les fonctions du package plyr . Les fonctions du même type du package base seront tout de même présentées par la suite. Les fonctions de l’extension plyr Les fonctions que nous allons aborder dans cette section possèdent des noms faciles à se remémorer : la première lettre correspond au format d’entrée des données, la seconde au format de sortie souhaité, et la fin du nom se termine par le suffixe ply . Ainsi, la fonction llpply prend en entrée une liste, effectue une opération sur les éléments, et retourne une liste (Anderson, 2012). Les différentes fonctions que nous allons passer en revue sont consignées dans le tableau ci-après, où les lignes correspondent aux formats d’entrée, et les lignes aux formats de sortie. Pour y avoir accès, il faut charger le package : library (plyr) ---------------------------------------------------------- You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr) ---------------------------------------------------------- Attaching package: 'plyr' The following object is masked from 'package:ggpubr':

mutate The following objects are masked from 'package:dplyr':

arrange, count, desc, failwith, id, mutate,
rename, summarise, summarize Attaching package: 'kableExtra' The following object is masked from 'package:dplyr':

group_rows Format de sortie array data.frame list Format d’entée array aaply adply alply data.frame daply ddply dlply list laply ldply llply Il est possible d’avoir plusieurs paramètres en input au lieu d’un seul objet. Les fonctions mlply, mdply et maply . Si à la place du m, la première lettre est un r, il s’agit alors de fonction de réplications. Enfin, si la seconde lettre est un trait de soulignement (_), alors le résultat retourné n’est pas affiché (le code utilise la fonction invisible . Tous les paramètres de ces fonctions commencent par un point (.), afin d’éviter des incompatibilités avec la fonction à appliquer. Array en input : a*ply Les fonctions aaply, adply et alply appliquent une fonction à chaque portion d’un array et ensuitent joignent le résultat sous forme d’un array, d’un data.frame ou d’une list respectivement. Un array peut être vu comme un vecteur à plusieurs dimensions. Comme pour un vecteur, toutes les valeurs doivent être du même type. Un vecteur n’est finalement qu’un array à une seule dimension. De même, un array à deux dimensions correspond à ce qu’on appelle usuelement une matrice. Le paramètre .margins détermine la manière de découper le tableau. Il y en a quatre pour un tableau en deux dimensions : .margins = 1 : par lignes ; .margins = 2 : par colonnes ; .margins = c(1,2) : par cellule ; .margins = c() : ne pas faire de découpement. Pour un tableau en trois dimensions, il y a trois découpages possibles en deux dimensions, trois en une dimension et une en zéro dimension (voir (Wickham, 2011)) au besoin. tableau <- array (1 : 24, dim = c (3, 4, 2), dimnames = list (ligne = letters[1 : 3], colonne = LETTERS[1 : 4], annee = 2001 : 2002)) tableau, , annee = 2001

colonne
ligne A B C D
a 1 4 7 10
b 2 5 8 11
c 3 6 9 12

, , annee = 2002

colonne
ligne A B C D
a 13 16 19 22
b 14 17 20 23
c 15 18 21 24 # La moyenne des valeurs pour chaque ligne aaply (tableau, 1, mean) # résultat sous forme de tableau a b c
11.5 12.5 13.5 adply (tableau, 1, mean) # résultat sous forme de data.frame {"columns":[{"label":["ligne"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["V1"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"a","2":"11.5"},{"1":"b","2":"12.5"},{"1":"c","2":"13.5"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} alply (tableau, 1, mean) # résultat sous forme de liste $`1`
[1] 11.5

$`2`
[1] 12.5

$`3`
[1] 13.5

attr(,"split_type")
[1] "array"
attr(,"split_labels")
ligne
1 a
2 b
3 c # La moyenne des valeurs pour chaque colonne en ne # simplifiant pas le résultat aaply (tableau, 2, mean, .drop = FALSE) colonne 1
A 8
B 11
C 14
D 17 # Par lignes et colonnes aaply (tableau, c (1, 2), mean) colonne
ligne A B C D
a 7 10 13 16
b 8 11 14 17
c 9 12 15 18 adply (tableau, c (1, 2), mean) {"columns":[{"label":["ligne"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["colonne"],"name":[2],"type":["fctr"],"align":["left"]},{"label":["V1"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"a","2":"A","3":"7"},{"1":"b","2":"A","3":"8"},{"1":"c","2":"A","3":"9"},{"1":"a","2":"B","3":"10"},{"1":"b","2":"B","3":"11"},{"1":"c","2":"B","3":"12"},{"1":"a","2":"C","3":"13"},{"1":"b","2":"C","3":"14"},{"1":"c","2":"C","3":"15"},{"1":"a","2":"D","3":"16"},{"1":"b","2":"D","3":"17"},{"1":"c","2":"D","3":"18"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # Avec une fonction définie par l'utilisateur standardise <- function (x) (x - min (x)) / (max (x) - min (x)) # Standardiser les valeurs par colonne aaply (tableau, 2, standardise), , annee = 2001

ligne
colonne a b c
A 0 0.07142857 0.1428571
B 0 0.07142857 0.1428571
C 0 0.07142857 0.1428571
D 0 0.07142857 0.1428571

, , annee = 2002

ligne
colonne a b c
A 0.8571429 0.9285714 1
B 0.8571429 0.9285714 1
C 0.8571429 0.9285714 1
D 0.8571429 0.9285714 1 Data.frame en input : d*ply Dans le cas de l’analyse d’enquêtes, on utilise principalement des tableaux de données ou data.frame . Aussi, la connaissance des fonction daply, ddply et dlply peut être utile. En effet, elles sont très utiles pour appliquer des fonctions à des groupes basés sur des combinaisons de variables, même si dans la majorité des cas il est maintenant plus facile de passer par les extensions dplyr ou data.table avec les opérations groupées (voir la section sur groub_by de dplyr ou encore celle sur le paramètre by de data.table . Avec les fonctions d*ply, il est nécessaire d’indiquer quelles variables, ou fonctions de variables on souhaite utiliser, en l’indiquant au paramètre .variables . Elles peuvent être contenue dans le data frame fourni au paramètre .data, ou bien provenir de l’environnement global. R cherchera dans un premier temps si la variable est contenue dans le data.frame et, s’il ne trouve pas, ira chercher dans l’environnement global. Pour indiquer que l’on désire faire le regroupement selon une variable - mettons variable_1 - il faudra fournir l’expression .(variable_1) au paramètre .variables . Si on souhaite effectuer les regroupement selon les interactions de plusieurs variables - variable_1, variable_2 et variable_3, il faut alors utiliser l’expression suivante : .(variable_1, variable_2, variable_3) . chomage <- data.frame (region = rep (c (rep ("Bretagne", 4), rep ("Corse", 2)), 2), departement = rep (c ("Cotes-d'Armor", "Finistere", "Ille-et-Vilaine", "Morbihan", "Corse-du-Sud", "Haute-Corse"), 2), annee = rep (c (2011, 2010), each = 6), ouvriers = c (8738, 12701, 11390, 10228, 975, 1297, 8113, 12258, 10897, 9617, 936, 1220), ingenieurs = c (1420, 2530, 3986, 2025, 259, 254, 1334, 2401, 3776, 1979, 253, 241)) chomage {"columns":[{"label":["region"],"name":[1],"type":["chr"],"align":["left"]},{"label":["departement"],"name":[2],"type":["chr"],"align":["left"]},{"label":["annee"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["ouvriers"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["ingenieurs"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"Bretagne","2":"Cotes-d'Armor","3":"2011","4":"8738","5":"1420"},{"1":"Bretagne","2":"Finistere","3":"2011","4":"12701","5":"2530"},{"1":"Bretagne","2":"Ille-et-Vilaine","3":"2011","4":"11390","5":"3986"},{"1":"Bretagne","2":"Morbihan","3":"2011","4":"10228","5":"2025"},{"1":"Corse","2":"Corse-du-Sud","3":"2011","4":"975","5":"259"},{"1":"Corse","2":"Haute-Corse","3":"2011","4":"1297","5":"254"},{"1":"Bretagne","2":"Cotes-d'Armor","3":"2010","4":"8113","5":"1334"},{"1":"Bretagne","2":"Finistere","3":"2010","4":"12258","5":"2401"},{"1":"Bretagne","2":"Ille-et-Vilaine","3":"2010","4":"10897","5":"3776"},{"1":"Bretagne","2":"Morbihan","3":"2010","4":"9617","5":"1979"},{"1":"Corse","2":"Corse-du-Sud","3":"2010","4":"936","5":"253"},{"1":"Corse","2":"Haute-Corse","3":"2010","4":"1220","5":"241"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # Total chomeurs en Bretagne et en Corse pour les années 2010 # et 2011 sous forme de data.frame ddply (chomage, .(annee), summarise, total_chomeurs = sum (ouvriers + ingenieurs)) {"columns":[{"label":["annee"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["total_chomeurs"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"2010","2":"53025"},{"1":"2011","2":"55803"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # sous forme de array daply (chomage, .(annee), summarise, total_chomeurs = sum (ouvriers + ingenieurs)) $`2010`
[1] 53025

$`2011`
[1] 55803 # sous forme de list dlply (chomage, .(annee), summarise, total_chomeurs = sum (ouvriers + ingenieurs)) $`2010`
total_chomeurs
1 53025

$`2011`
total_chomeurs
1 55803

attr(,"split_type")
[1] "data.frame"
attr(,"split_labels")
annee
1 2010
2 2011 # Total chomeurs pour les années 2010 et 2011, par région du # data frame ddply (chomage, .(annee, region), summarise, total_chomeurs = sum (ouvriers + ingenieurs)) {"columns":[{"label":["annee"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["region"],"name":[2],"type":["chr"],"align":["left"]},{"label":["total_chomeurs"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"2010","2":"Bretagne","3":"50375"},{"1":"2010","2":"Corse","3":"2650"},{"1":"2011","2":"Bretagne","3":"53018"},{"1":"2011","2":"Corse","3":"2785"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # Nombre d'observations pour chaque groupe ddply (chomage, .(annee, region), nrow) {"columns":[{"label":["annee"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["region"],"name":[2],"type":["chr"],"align":["left"]},{"label":["V1"],"name":[3],"type":["int"],"align":["right"]}],"data":[{"1":"2010","2":"Bretagne","3":"4"},{"1":"2010","2":"Corse","3":"2"},{"1":"2011","2":"Bretagne","3":"4"},{"1":"2011","2":"Corse","3":"2"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # En utilisant une fonction définie par l'utilisateur ddply (chomage, .(annee, region), function (x) {moy_ouvriers <- mean (x $ ouvriers) moy_ingenieurs <- mean (x $ ingenieurs) data.frame (moy_ouvriers = moy_ouvriers, moy_ingenieurs = moy_ingenieurs)}) {"columns":[{"label":["annee"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["region"],"name":[2],"type":["chr"],"align":["left"]},{"label":["moy_ouvriers"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["moy_ingenieurs"],"name":[4],"type":["dbl"],"align":["right"]}],"data":[{"1":"2010","2":"Bretagne","3":"10221.25","4":"2372.50"},{"1":"2010","2":"Corse","3":"1078.00","4":"247.00"},{"1":"2011","2":"Bretagne","3":"10764.25","4":"2490.25"},{"1":"2011","2":"Corse","3":"1136.00","4":"256.50"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} List en input : l*ply Les fonctions du type l*ply prennent une liste en entrée. Il n’y a donc pas de paramétrage à effectuer pour choisir un découpage, il est déjà fait. set.seed (1) liste <- list (normale = rnorm (10), logiques = c (TRUE, TRUE, FALSE), x = c (0, NA, 3)) # Obtenir la longueur de chaque élément de la liste laply (liste, length) [1] 10 3 3 ldply (liste, length) {"columns":[{"label":[".id"],"name":[1],"type":["chr"],"align":["left"]},{"label":["V1"],"name":[2],"type":["int"],"align":["right"]}],"data":[{"1":"normale","2":"10"},{"1":"logiques","2":"3"},{"1":"x","2":"3"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} llply (liste, length) $normale
[1] 10

$logiques
[1] 3

$x
[1] 3 # Calculer la moyenne pour chaque élément unlist (llply (liste, mean, na.rm = TRUE)) normale logiques x
0.1322028 0.6666667 1.5000000 # Appliquer une fonction définie par l'utilisateur llply (liste, function (x, y) x / mean (x, na.rm = TRUE) + y, y = 2) $normale
[1] -2.7385827 3.3891033 -4.3208096 14.0669232 4.4924421
[6] -4.2061356 5.6869803 7.5847895 6.3552892 -0.3099997

$logiques
[1] 3.5 3.5 2.0

$x
[1] 2 NA 4 Calcul parallèle En utilisant plusieurs processeurs, on peut effectuer des calculs parallèles, ce qui accélère les calculs dans certains cas. En effet, quand il est possible de fractionner les opérations à effectuer en morceaux, on peut en réaliser une partie sur un processeur, une autre sur un second processeur, et ainsi de suite. Les résultats obtenus sont ensuite rassemblés avant d’être retournés. Le package doMC (ou doSMP sur Windows) peut être chargé pour utiliser la fonction de calcul parallèle proposé par les fonctions **ply . Il suffit de préciser le nombre de coeurs souhaité en faisant appel à la fonction registerDoMC, et de fixer la valeur TRUE au paramètre .parallel de la fonction **ply . Les fonctions de la famille apply du package base Le tableau ci-après recense les fonctions principales de la famille apply du package base . Fonction Input Output apply Matrice ou tableau Vecteur ou tableau ou liste lapply Liste ou vecteur Liste sapply Liste ou vecteur Vecteur ou matrice ou liste vapply Liste ou vecteur Vecteur ou matrice ou liste tapply Tableau et facteurs Tableau ou liste mapply Listes et/ou vecteurs Vecteur ou matrice ou liste La fonction lapply La fonction lapply applique à chaque élément du premier paramètre qui lui est donné une fonction indiquée en second paramètre et retourne le résultat sous forme de liste. La syntaxe est la suivante : lapply (X, FUN, ...) avec X la liste ou le vecteur donné en paramètre sur lequel on désire appliquer la fonction FUN . La paramètre ... permet de fournir des paramètres à une fonction imbriquée, en l’occurance à celle que l’on souhaite appliquer à tous les éléments de X . liste <- list (normale = rnorm (10), logiques = c (TRUE, TRUE, FALSE), x = c (0, NA, 3)) # Obtenir la liste des longueurs de chaque élément lapply (liste, length) $normale
[1] 10

$logiques
[1] 3

$x
[1] 3 # Calculer la moyenne pour chaque élément lapply (liste, mean, na.rm = TRUE) $normale
[1] 0.248845

$logiques
[1] 0.6666667

$x
[1] 1.5 On peut créer une fonction à l’intérieur de l’appel à lapply . Le premier paramètre est nécessairement un élément du vecteur auquel on souhaite appliquer la fonction. lapply (liste, function (x) x / mean (x, na.rm = TRUE)) $normale
[1] 6.07519277 1.56661087 -2.49649643 -8.89991820
[5] 4.52060941 -0.18056868 -0.06506164 3.79286833
[9] 3.30013177 2.38663180

$logiques
[1] 1.5 1.5 0.0

$x
[1] 0 NA 2 # Si la fonction doit posséder plusieurs paramètres lapply (liste, function (x, y) x / mean (x, na.rm = TRUE) + y, y = 2) $normale
[1] 8.0751928 3.5666109 -0.4964964 -6.8999182 6.5206094
[6] 1.8194313 1.9349384 5.7928683 5.3001318 4.3866318

$logiques
[1] 3.5 3.5 2.0

$x
[1] 2 NA 4 On peut appliquer la lapply sur des tableaux de données, dans la mesure où ces derniers sont des listes. Cela s’avère pratique pour réaliser des opérations pour chaque colonne d’un tableau de données. Afin de prendre moins de place dans l’affichage, l’exemple suivant utilise la fonction unlist pour aplatir la liste obtenue. data (cars) # Afficher le type de chaque colonne de la data frame 'cars' unlist (lapply (cars, class)) speed dist
"numeric" "numeric" # Calculer la moyenne pour chaque colonne unlist (lapply (cars, mean)) speed dist
15.40 42.98 Attention, ce qui suit relève plus d’un tour de passe-passe que de la programmation élégante. Si la fonction que l’on souhaite appliquer aux éléments de notre vecteur retourne un vecteur ligne de même longueur pour chaque élément, la fonction do.call peut devenir un outil très pratique pour créer une data frame. Voyons-le à travers un exemple. l <- lapply (1 : 3, function (x) cbind (valeur = x, lettre = LETTERS[x])) l [[1]]
valeur lettre
[1,] "1" "A"

[[2]]
valeur lettre
[1,] "2" "B"

[[3]]
valeur lettre
[1,] "3" "C" data.frame (do.call ("rbind", l)) {"columns":[{"label":["valeur"],"name":[1],"type":["chr"],"align":["left"]},{"label":["lettre"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"1","2":"A"},{"1":"2","2":"B"},{"1":"3","2":"C"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} L’appel de do.call("rbind", x) revient à faire rbind(x[[1]], x[[2]], ..., x[[n]]) avec x une liste de taille n . La fonction sapply La fonction sapply applique une fonction aux éléments d’un vecteur ou d’une liste et peut retourner un vecteur, une liste ou une matrice. Elle possède la syntaxe suivante : sapply (X, FUN, simplify, USE.NAMES) où X est le vecteur ou la liste auquel on souhaite appliquer la fonction FUN . Lorsque simplify vaut FALSE, le résultat est retourné sous forme de liste, exactement comme lapply (la fonction sapply s’appuie sur la fonction lapply). Lorsque simplify vaut TRUE (par défaut), le résultat est retourné dans une forme simplifiée, si cela est possible. Si tous les éléments retournés par la fonction FUN sont des scalaires, alors sapply retourne un vecteur ; sinon, si les éléments retournés ont la même taille, sapply retourne une matrice avec une colonne pour chaque élément de X auquel la fonction FUN est appliquée. Le paramètre USE.NAMES, quand il vaut TRUE (par défaut), et si X est de type character, utilise X comme nom pour le résultat, à moins que le résultat possède déjà des noms. (x <- list (a = 1 : 10, beta = exp (- 3 : 3), logic = c (TRUE, FALSE, FALSE, TRUE))) $a
[1] 1 2 3 4 5 6 7 8 9 10

$beta
[1] 0.04978707 0.13533528 0.36787944 1.00000000
[5] 2.71828183 7.38905610 20.08553692

$logic
[1] TRUE FALSE FALSE TRUE # Application de la fonction quantile() à chaque élément pour # obtenir la médiane et les quartiles Avec lapply() lapply (x, quantile) $a
0% 25% 50% 75% 100%
1.00 3.25 5.50 7.75 10.00

$beta
0% 25% 50% 75% 100%
0.04978707 0.25160736 1.00000000 5.05366896 20.08553692

$logic
0% 25% 50% 75% 100%
0.0 0.0 0.5 1.0 1.0 # Avec sapply sapply (x, quantile) a beta logic
0% 1.00 0.04978707 0.0
25% 3.25 0.25160736 0.0
50% 5.50 1.00000000 0.5
75% 7.75 5.05366896 1.0
100% 10.00 20.08553692 1.0 # Exemple avec USE.NAMES sapply (LETTERS[1 : 3], nchar) A B C
1 1 1 sapply (LETTERS[1 : 3], nchar, USE.NAMES = FALSE) [1] 1 1 1 La fonction vapply La fonction vapply est similaire à sapply, mais elle possède un type de valeurs spécifié, ce qui peut rendre l’utilisation plus sûre (et parfois plus rapide). Lorsqu’on lui fournit un data.frame, vapply retourne le même résultat que sapply . Cependant, quand on lui fournit une liste vide, vapply retourne un vecteur logique de longueur nulle (ce qui est plus sensé que la liste vide que returne sapply). vapply (X, FUN, FUN.VALUE, ..., USE.NAMES) avec X, FUN, ... et USE.NAMES les mêmes paramètres que pour sapply . Le paramètre FUN.VALUE doit être un vecteur, un masque pour la valeur retournée par la fonction de FUN . # Retourner le vecteur sapply (cars, is.numeric) speed dist
TRUE TRUE vapply (cars, is.numeric, FUN.VALUE = logical (1)) speed dist
TRUE TRUE # Avec la liste vide sapply (list (), is.numeric) list() vapply (list (), is.numeric, FUN.VALUE = logical (1)) logical(0) La fonction apply La fonction apply possède la syntaxe suivante : apply (X, MARGIN, FUN, ...) avec X une matrice ou un tableau, MARGIN indiquant si on souhaite appliquer la fonction FUN aux lignes (MARGIN = 1) ou aux colonnes (MARGIN = 2), et ... des paramètres supplémentaires éventuels à passer à la fonction FUN . (X <- matrix (1 : 9, ncol = 3)) [,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9 # Somme par ligne apply (X, MARGIN = 1, sum) [1] 12 15 18 # Somme par colonne apply (X, MARGIN = 2, sum) [1] 6 15 24 # Fonction définie par l'utilisateur apply (X, MARGIN = 1, function (x) sum (x) / sum (X)) [1] 0.2666667 0.3333333 0.4000000 La fonction tapply La fonction tapply s’applique à chaque cellule d’un tableau, sur des regroupements définis par les variables catégorielles fournies. La syntaxe est la suivante : tapply (X, INDEX, FUN, ..., simplify) avec X le tableau de données, INDEX une liste d’un ou plusieurs facteurs, chacun de même taille que X . Le paramètre FUN renseigne la fonction que l’on souhaite appliquer. Si simplify vaut FALSE, le résultat est un tableau de mode list . Sinon (par défaut), le résultat est un tableau de scalaires. data (iris) head (iris) {"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Sepal.Length"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["Sepal.Width"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Petal.Length"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["Petal.Width"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Species"],"name":[5],"type":["fctr"],"align":["left"]}],"data":[{"1":"5.1","2":"3.5","3":"1.4","4":"0.2","5":"setosa","_rn_":"1"},{"1":"4.9","2":"3.0","3":"1.4","4":"0.2","5":"setosa","_rn_":"2"},{"1":"4.7","2":"3.2","3":"1.3","4":"0.2","5":"setosa","_rn_":"3"},{"1":"4.6","2":"3.1","3":"1.5","4":"0.2","5":"setosa","_rn_":"4"},{"1":"5.0","2":"3.6","3":"1.4","4":"0.2","5":"setosa","_rn_":"5"},{"1":"5.4","2":"3.9","3":"1.7","4":"0.4","5":"setosa","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}} # Moyenne de la longueur des sépales par espèce tapply (iris $ Sepal.Length, iris $ Species, mean) setosa versicolor virginica
5.006 5.936 6.588 # Pour retourner le résultat sous forme de liste tapply (iris $ Sepal.Length, iris $ Species, mean, simplify = FALSE) $setosa
[1] 5.006

$versicolor
[1] 5.936

$virginica
[1] 6.588 La fonction mapply La fonction mapply applique une fonction à plusieurs listes ou vecteurs. La syntaxe est la suivante : mapply (FUN, ..., MoreArgs, SIMPLIFY, USE.NAMES) avec FUN la fonction à appliquer aux vecteurs ou listes fournies (grâce à ...), MoreArgs une liste de paramètres supplémentaires à fournir à la fonction à appliquer. Les paramètres SIMPLIFY et USE.NAMES ont le même usage que pour la fonction sapply . (l1 <- list (a = c (1 : 5), b = c (6 : 10))) $a
[1] 1 2 3 4 5

$b
[1] 6 7 8 9 10 (l2 <- list (c = c (11 : 15), d = c (16 : 20))) $c
[1] 11 12 13 14 15

$d
[1] 16 17 18 19 20 # La somme des éléments correspondants de l1 et l2 mapply (sum, l1 $ a, l1 $ b, l2 $ c, l2 $ d) [1] 34 38 42 46 50 # Attention au recyclage silencieux ! (l1 <- list (a = c (1 : 5), b = c (6 : 20))) $a
[1] 1 2 3 4 5

$b
[1] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 mapply (sum, l1 $ a, l1 $ b, l2 $ c, l2 $ d) [1] 34 38 42 46 50 39 43 47 51 55 44 48 52 56 60 La fonction Vectorize La fonction Vectorize permet de convertir une fonction scalaire en une fonction vectorielle. Attention, cela ne permet pas d’améliorer la rapidité d’exécution du code. Par contre, son utilisation assez intuitive permet de gagner du temps. Il s’agit donc de faire l’arbitrage entre le temps passé à trouver un moyen élégant et efficace pour effectuer une opération en passant par de réels calculs vectoriels et le gain d’exécution que ce calcul vectoriel apporte vis-à-vis d’une boucle. La syntaxe de la Vectorize est la suivante : Vectorize (FUN, vectorize.args, SIMPLIFY, USE.NAMES) avec FUN une fonction à appliquer, vectorize.args un vecteur de paramètres (de type caractère) qui devraient être vectorisés (par défaut, tous les paramètre de FUN). Les paramètres SIMPLIFY et USE.NAMES on le même emploi que dans la fonction sapply . f <- function (x = 1 : 3, y) c (x, y) # On 'vectorise' la fonction f vf <- Vectorize (f, SIMPLIFY = FALSE) f (1 : 3, 1 : 3) [1] 1 2 3 1 2 3 vf (1 : 3, 1 : 3) [[1]]
[1] 1 1

[[2]]
[1] 2 2

[[3]]
[1] 3 3 # Vectorise seulement y, pas x vf (y = 1 : 3) [[1]]
[1] 1 2 3 1

[[2]]
[1] 1 2 3 2

[[3]]
[1] 1 2 3 3 Expressions régulières Les expressions régulières sont un outils pour rechercher / remplacer dans des chaînes de texte. Il est préférable d’avoir lu au préalable le chapitre dédié à la manipulation de texte . Pour une introduction (en anglais) aux expressions régulières, on pourra se référer au chapitre Strings de l’ouvrage R for Data Science de Garrett Grolemund et Hadley Wickham (http://r4ds.had.co.nz/strings.html). Pour aller plus loin, le site de l’extension stringr propose une présentation détaillée (en anglais) de la syntaxe des expressions régulières (http://stringr.tidyverse.org/articles/regular-expressions.html). Pour des besoins plus pointus, on pourra aussi utiliser l’extension stringi sur laquelle est elle-même basée stringr . R Markdown : les rapports automatisés Créer un nouveau document Élements d’un document R Markdown En-tête (préambule) Texte du document Blocs de code R Compiler un document (Knit) Personnaliser le document généré Options des blocs de code R Nom du bloc Options Modifier les options Options globales Mise en cache des résultats Rendu des tableaux Tableaux croisés Tableaux de données et tris à plat Autres extensions pour présenter des tableaux Modèles de documents Slides Templates Ressources La version originale de ce chapitre a été écrite par Julien Barnier dans le cadre de son Introduction à R et au tidyverse . L’extension rmarkdown permet de générer des documents de manière dynamique en mélangeant texte mis en forme et résultats produits par du code R . Les documents générés peuvent être au format HTML, PDF, Word, et bien d’autres 1 . C’est donc un outil très pratique pour l’exportation, la communication et la diffusion de résultats d’analyse. Le présent document a lui-même été généré à partir de fichiers R Markdown . rmarkdown ne fait pas partie du tidyverse, mais elle est installée et chargée par défaut par RStudio 2 . Voici un exemple de document R Markdown minimal : ---
title: "Test R Markdown"
---

*R Markdown* permet de mélanger :

- du texte libre mis en forme
- des blocs de code R

Les blocs de code sont exécutés et leur résultat affiché, par exemple :

```{r}
mean(mtcars$mpg)
```

## Graphiques

On peut également inclure des graphiques :

```{r}
plot(mtcars$hp, mtcars$mpg)
``` Ce document peut être compilé sous différents formats. Lors de cette étape, le texte est mis en forme, les blocs de code sont exécutés, leur résultat ajouté au document, et le tout est transformé dans un des différents formats possibles. Voici le rendu du document précédent au format HTML : Rendu HTML Le rendu du même document au format PDF : Rendu PDF Et le rendu au format Word : Rendu docx Les avantages de ce système sont nombreux : le code et ses résultats ne sont pas séparés des commentaires qui leur sont associés le document final est reproductible le document peut être très facilement régénéré et mis à jour, par exemple si les données source ont été modifiées. Créer un nouveau document Un document R Markdown est un simple fichier texte enregistré avec l’extension .Rmd . Sous RStudio, on peut créer un nouveau document en allant dans le menu File puis en choisissant New file puis R Markdown\dots . La boîte de dialogue suivante s’affiche : Création d’un document R Markdown On peut indiquer le titre, l’auteur du document ainsi que le format de sortie par défaut (il est possible de modifier facilement ses éléments par la suite). Plutôt qu’un document classique, on verra plus loin qu’on peut aussi choisir de créer une présentation sous forme de slides (entrée Presentation) ou de créer un document à partir d’un modèle (Entrée From Template). Un fichier comportant un contenu d’exemple s’affiche alors. Vous pouvez l’enregistrer où vous le souhaitez avec une extension .Rmd . Élements d’un document R Markdown Un document R Markdown est donc un fichier texte qui ressemble à quelque chose comme ça : ---
title: "Titre"
author: "Prénom Nom"
date: "10 avril 2017"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Introduction

Ceci est un document RMarkdown, qui mélange :

- du texte balisé selon la syntaxe Markdown
- des bouts de code R qui seront exécutés

Le code R se présente de la manière suivante :

```{r}
summary(cars)
```

## Graphiques

On peut aussi inclure des graphiques, par exemple :

```{r}
plot(pressure)
``` En-tête (préambule) La première partie du document est son en-tête . Il se situe en tout début de document, et est délimité par trois tirets (---) avant et après : ---
title: "Titre"
author: "Prénom Nom"
date: "10 avril 2017"
output: html_document
--- Cet en-tête contient les métadonnées du document, comme son titre, son auteur, sa date, plus tout un tas d’options possibles qui vont permettre de configurer ou personnaliser l’ensemble du document et son rendu. Ici, par exemple, la ligne output: html_document indique que le document généré doit être au format HTML. Texte du document Le corps du document est constitué de texte qui suit la syntaxe Markdown . Un fichier Markdown est un fichier texte contenant un balisage léger qui permet de définir des niveaux de titres ou de mettre en forme le texte. Par exemple, le texte suivant : Ceci est du texte avec *de l'italique* et **du gras**.

On peut définir des listes à puces :

- premier élément
- deuxième élément Génèrera le texte mis en forme suivant : Ceci est du texte avec de l’ italique et du gras . On peut définir des listes à puces : premier élément deuxième élément On voit que des mots placés entre des astérisques sont mis en italique, des lignes qui commencent par un tiret sont transformés en liste à puce, etc. On peut définir des titres de différents niveaux en faisant débuter une ligne par un ou plusieurs caractères # : # Titre de niveau 1

## Titre de niveau 2

### Titre de niveau 3 Quand des titres ont été définis, si vous cliquez sur l’icône Show document outline totalement à droite de la barre d’outils associée au fichier R Markdown, une table des matières dynamique générée automatiquement à partir des titres présents dans le document s’affiche et vous permet de naviguer facilement dans celui-ci : Table des matières dynamique La syntaxe Markdown permet d’autres mises en forme, comme la possibilité d’insérer des liens ou des images. Par exemple, le code suivant : [Exemple de lien](https://example.com) Donnera le lien suivant : Exemple de lien Dans RStudio, le menu Help puis Markdown quick reference donne un aperçu plus complet de la syntaxe. Blocs de code R En plus du texte libre au format Markdown, un document R Markdown contient, comme son nom l’indique, du code R . Celui-ci est inclus dans des blocs (chunks) délimités par la syntaxe suivante : ```{r}
x <- 1:5
``` Comme cette suite de caractères n’est pas très simple à saisir, vous pouvez utiliser le menu Insert de RStudio et choisir R 3, ou utiliser le raccourci clavier Ctrl+Alt+i . Menu d’insertion d’un bloc de code Dans RStudio les blocs de code R sont en général affichés avec une couleur de fond légèrement différente pour les distinguer du reste du document. Quand votre curseur se trouve dans un bloc, vous pouvez saisir le code R que vous souhaitez, l’exécuter, utiliser l’autocomplétion, exactement comme si vous vous trouviez dans un script R . Vous pouvez également exécuter l’ensemble du code contenu dans un bloc à l’aide du raccourci clavier Ctrl+Shift+Entrée . Dans RStudio, par défaut, les résultats d’un bloc de code (texte, tableau ou graphique) s’affichent directement dans la fenêtre d’édition du document, permettant de les visualiser facilement et de les conserver le temps de la session 4 . Lorsque le document est compilé au format HTML, PDF ou docx, chaque bloc est exécuté tour à tour, et le résultat inclus dans le document final, qu’il s’agisse de texte, d’un tableau ou d’un graphique. Les blocs sont liés entre eux, dans le sens où les données importées ou calculées dans un bloc sont accessibles aux blocs suivants. On peut donc aussi voir un document R Markdown comme un script R dans lequel on aurait intercalé du texte libre au format Markdown . À noter qu’avant chaque compilation, une nouvelle session R est lancée, ne contenant aucun objet. Les premiers blocs de code d’un document sont donc souvent utilisés pour importer des données, exécuter des recodages, etc. Compiler un document (Knit) On peut à tout moment compiler, ou plutôt tricoter (Knit), un document R Markdown pour obtenir et visualiser le document généré. Pour cela, il suffit de cliquer sur le bouton Knit et de choisir le format de sortie voulu : Menu Knit Vous pouvez aussi utiliser le raccourci Ctrl+Shift+K pour compiler le document dans le dernier format utilisé. Pour la génération du format PDF, vous devez avoir une installation fonctionnelle de LaTeX sur votre système. C’est en général le cas pour des ordinateurs Mac ou Linux, mais pas sous Windows : dans ce cas vous devrez installer une distribution comme MiKTeX . Un onglet R Markdown s’ouvre dans la même zone que l’onglet Console et indique la progression de la compilation, ainsi que les messages d’erreur éventuels. Si tout se passe bien, Le document devrait s’afficher soit dans une fenêtre Viewer de RStudio (pour la sortie HTML), soit dans le logiciel par défaut de votre ordinateur. Personnaliser le document généré La personnalisation du document généré se fait en modifiant des options dans le préambule du document. RStudio propose néanmoins une petite interface graphique permettant de changer ces options plus facilement. Pour cela, cliquez sur l’icône en forme d’engrenage à droite du bouton Knit et choisissez Output Options\dots Options de sortie R Markdown Une boîte de dialogue s’affiche vous permettant de sélectionner le format de sortie souhaité et, selon le format, différentes options : Dialogue d’options de sortie R Markdown Pour le format HTML par exemple, l’onglet General vous permet de spécifier si vous voulez une table des matières, sa profondeur, les thèmes à appliquer pour le document et la coloration syntaxique des blocs R, etc. L’onglet Figures vous permet de changer les dimensions par défaut des graphiques générés. Une option très intéressante pour les fichiers HTML, accessible via l’onglet Advanced, est l’entrée Create standalone HTML document . Si elle est cochée (ce qui est le cas par défaut), le document HTML généré contiendra en un seul fichier le code HTML mais aussi les images et toutes les autres ressources nécessaires à son affichage. Ceci permet de générer des fichiers (parfois assez volumineux) que vous pouvez transférer très facilement à quelqu’un par mail ou en le mettant en ligne quelque part. Si la case n’est pas cochée, les images et autres ressources sont placées dans un dossier à part. Lorsque vous changez des options, RStudio va en fait modifier le préambule de votre document. Ainsi, si vous choisissez d’afficher une table des matières et de modifier le thème de coloration syntaxique, votre en-tête va devenir quelque chose comme : ---
title: "Test R Markdown"
output:
html_document:
highlight: kate
toc: yes
--- Vous pouvez modifier les options directement en éditant le préambule. À noter qu’il est possible de spécifier des options différentes selon les formats, par exemple : ---
title: "Test R Markdown"
output:
html_document:
highlight: kate
toc: yes
pdf_document:
fig_caption: yes
highlight: kate
--- La liste complète des options possibles est présente sur le site de la documentation officielle (très complet et bien fait) et sur l’antisèche et le guide de référence, accessibles depuis RStudio via le menu Help puis Cheatsheets . Options des blocs de code R Il est également possible de passer des options à chaque bloc de code R pour modifier son comportement. On rappelle qu’on bloc de code se présente de la manière suivante : ```{r}
x <- 1:5
``` Les options d’un bloc de code sont à placer à l’intérieur des accolades {r} . Nom du bloc La première possibilité est de donner un nom au bloc. Celui-ci est indiqué directement après le r : {r nom_du_bloc} Il n’est pas obligatoire de nommer un bloc, mais cela peut être utile en cas d’erreur à la compilation, pour identifier le bloc ayant causé le problème. Attention, on ne peut pas avoir deux blocs avec le même nom. Options En plus d’un nom, on peut passer à un bloc une série d’options sous la forme option = valeur . Voici un exemple de bloc avec un nom et des options : ```{r mon_bloc, echo = FALSE, warning = TRUE}
x <- 1:5
``` Et un exemple de bloc non nommé avec des options : ```{r echo = FALSE, warning = FALSE}
x <- 1:5
``` Une des options la plus utile est l’option echo . Par défaut echo vaut TRUE, et le bloc de code R est inséré dans le document généré, de cette manière : x <- 1 : 5 print (x) [1] 1 2 3 4 5 Mais si on positionne l’option echo=FALSE, alors le code R n’est plus inséré dans le document, et seul le résultat est visible : [1] 1 2 3 4 5 Voici une liste de quelques unes des options disponibles : Option Valeurs Description echo TRUE/FALSE Afficher ou non le code R dans le document eval TRUE/FALSE Exécuter ou non le code R à la compilation include TRUE/FALSE Inclure ou non le code R et ses résultats dans le document results “hide”/“asis”/“markup”/“hold” Type de résultats renvoyés par le bloc de code warning TRUE/FALSE Afficher ou non les avertissements générés par le bloc message TRUE/FALSE Afficher ou non les messages générés par le bloc Il existe de nombreuses autres options décrites notamment dans guide de référence R Markdown (PDF en anglais). Modifier les options Il est possible de modifier les options manuellement en éditant l’en-tête du bloc de code, mais on peut aussi utiliser une petite interface graphique proposée par RStudio . Pour cela, il suffit de cliquer sur l’icône d’engrenage située à droite sur la ligne de l’en-tête de chaque bloc : Menu d’options de bloc de code Vous pouvez ensuite modifier les options les plus courantes, et cliquer sur Apply pour les appliquer. Options globales On peut vouloir appliquer une option à l’ensemble des blocs d’un document. Par exemple, on peut souhaiter par défaut ne pas afficher le code R de chaque bloc dans le document final. On peut positionner une option globalement en utilisant la fonction knitr::opts_chunk$set() . Par exemple, insérer knitr::opts_chunk$set(echo = FALSE) dans un bloc de code positionnera l’option echo = FALSE par défaut pour tous les blocs suivants. En général, on place toutes ces modifications globales dans un bloc spécial nommé setup et qui est le premier bloc du document : ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
``` Par défaut RStudio exécute systématiquement le contenu du bloc setup avant d’exécuter celui d’un autre bloc. Mise en cache des résultats Compiler un document R Markdown peut être long, car il faut à chaque fois exécuter l’ensemble des blocs de code R qui le constituent. Pour accélérer cette opération, R Markdown utilise un système de mise en cache : les résultats de chaque bloc sont enregistrés dans un fichier et à la prochaine compilation, si le code et les options du bloc n’ont pas été modifiés, c’est le contenu du fichier de cache qui est utilisé, ce qui évite d’exécuter le code R. On peut activer ou désactiver la mise en cache des résultats pour chaque bloc de code avec l’option cache = TRUE ou cache = FALSE, et on peut aussi désactiver totalement la mise en cache pour le document en ajoutant knitr::opts_chunk$set(echo = FALSE) dans le premier bloc setup . Ce système de cache peut poser problème par exemple si les données source changent : dans ce cas les résultats de certains blocs peuvent ne pas être mis à jour s’ils sont présents en cache. Dans ce cas, on peut vider le cache du document, ce qui forcera un recalcul de tous les blocs de code à la prochaine compilation. Pour cela, vous pouvez ouvrir le menu Knit et choisir Clear Knitr Cache\dots : Menu Knit Rendu des tableaux Tableaux croisés Par défaut, les tableaux issus de la fonction table sont affichés comme ils apparaissent dans la console de R, en texte brut : library (questionr) data (hdv2003) tab <- lprop (table (hdv2003 $ qualif, hdv2003 $ sexe)) tab Homme Femme Total
Ouvrier specialise 47.3 52.7 100.0
Ouvrier qualifie 78.4 21.6 100.0
Technicien 76.7 23.3 100.0
Profession intermediaire 55.0 45.0 100.0
Cadre 55.8 44.2 100.0
Employe 16.2 83.8 100.0
Autre 36.2 63.8 100.0
Ensemble 44.8 55.2 100.0 On peut améliorer leur présentation en utilisant la fonction kable de l’extension knitr . Celle-ci fournit un formatage adapté en fonction du format de sortie. On aura donc des tableaux propres que ce soit en HTML, PDF ou aux formats traitements de texte : library (knitr) kable (tab) Homme Femme Total Ouvrier specialise 47.29064 52.70936 100 Ouvrier qualifie 78.42466 21.57534 100 Technicien 76.74419 23.25581 100 Profession intermediaire 55.00000 45.00000 100 Cadre 55.76923 44.23077 100 Employe 16.16162 83.83838 100 Autre 36.20690 63.79310 100 Ensemble 44.82759 55.17241 100 Différents arguments permettent de modifier la sortie de kable . digits, par exemple, permet de spécifier le nombre de chiffres significatifs à afficher dans les colonnes de nombres : kable (tab, digits = 1) Homme Femme Total Ouvrier specialise 47.3 52.7 100 Ouvrier qualifie 78.4 21.6 100 Technicien 76.7 23.3 100 Profession intermediaire 55.0 45.0 100 Cadre 55.8 44.2 100 Employe 16.2 83.8 100 Autre 36.2 63.8 100 Ensemble 44.8 55.2 100 Tableaux de données et tris à plat En ce qui concerne les tableaux de données (tibble ou data frame), l’affichage HTML par défaut se contente d’un affichage texte comme dans la console, très peu lisible dès que le tableau dépasse une certaine dimension. Une alternative est d’utiliser la fonction paged_table, qui affiche une représentation HTML paginée du tableau : rmarkdown :: paged_table (hdv2003) 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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>id<\/th>\n <th>age<\/th>\n <th>sexe<\/th>\n <th>nivetud<\/th>\n <th>poids<\/th>\n <th>occup<\/th>\n <th>qualif<\/th>\n <th>freres.soeurs<\/th>\n <th>clso<\/th>\n <th>relig<\/th>\n <th>trav.imp<\/th>\n <th>trav.satisf<\/th>\n <th>hard.rock<\/th>\n <th>lecture.bd<\/th>\n <th>peche.chasse<\/th>\n <th>cuisine<\/th>\n <th>bricol<\/th>\n <th>cinema<\/th>\n <th>sport<\/th>\n <th>heures.tv<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,5,8,20]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]} Dans tous les cas il est déconseillé d’afficher de cette manière un tableau de données de très grandes dimensions, car le fichier HTML résultant contiendrait l’ensemble des données et serait donc très volumineux. On peut définir un mode d’affichage par défaut pour tous les tableaux de données en modifiant les Output options du format HTML (onglet General, Print dataframes as), ou en modifiant manuellement l’option df_print de l’entrée html_document dans le préambule. À noter que les tableaux issus de la fonction freq de questionr s’affichent comme des tableaux de données (et non comme des tableaux croisés). Autres extensions pour présenter des tableaux Il existe de nombreuses extensions offrant des fonctionnalités de présentation enrichie des tableaux et autres objets R . printr L’extension printr développée par le même auteur que knitr étent le fonctionnement par défaut de knitr . Une fois chargée, le rendu automatique de certains objets (tel qu’un tableau croisé à trois variables) sera amélioré. library (printr) Registered S3 method overwritten by 'printr':
method from
knit_print.data.frame rmarkdown x1 = sample (letters[1 : 2], 1000, TRUE) x2 = sample (letters[1 : 2], 1000, TRUE) x3 = sample (letters[1 : 2], 1000, TRUE) table (x1, x2, x3) x1 x2 x3 Freq a a a 136 b 137 b a 108 b 135 b a a 126 b 110 b a 132 b 116 Pour d’autres exemples, voir la documentation de l’extension sur https://yihui.name/printr/ . kableExtra L’extension kableExtra a pour objectif d’étendre la fonction kable de knitr avec des options comme la possibilité de regrouper des colonnes, ajouter des notes de tableau, coloriser certaines cellules\dots library (kableExtra) dt <- mtcars[1 : 5, 1 : 4] kable (dt, format = "html", caption = "Demo Table") %>% kable_styling (bootstrap_options = "striped", full_width = F) %>% add_header_above (c (" ", ` Group 1 ` = 2, ` Group 2[note] ` = 2)) %>% add_footnote (c ("table footnote")) Demo Table Group 1 Group 2 a mpg cyl disp hp Mazda RX4 21.0 6160110 Mazda RX4 Wag 21.0 6160110 Datsun 710 22.8 410893 Hornet 4 Drive 21.4 6258110 Hornet Sportabout 18.7 8360175 a table footnote Pour une présentation détaillée, vous pouvez vous référer aux vignettes disponibles avec l’extension . Notamment, il est possible d’utiliser kableExtra en conjonction avec l’extension formattable pour des rendus colorés et encore plus personnalisé de vos tableaux (voir la vignette dédiée). library (knitr) library (kableExtra) library (formattable) library (dplyr, quietly = TRUE) Attaching package: 'dplyr' The following object is masked from 'package:kableExtra':

group_rows The following objects are masked from 'package:stats':

filter, lag The following objects are masked from 'package:base':

intersect, setdiff, setequal, union mtcars[1 : 5, 1 : 4] %>% mutate (car = row.names (.), mpg = color_tile ("white", "orange")(mpg), cyl = cell_spec (cyl, "html", angle = (1 : 5) * 60, background = "red", color = "white", align = "center"), disp = ifelse (disp > 200, cell_spec (disp, "html", color = "red", bold = T), cell_spec (disp, "html", color = "green", italic = T)), hp = color_bar ("lightgreen")(hp)) %>% select (car, everything ()) %>% kable ("html", escape = F) %>% kable_styling ("hover", full_width = F) %>% column_spec (5, width = "3cm") %>% add_header_above (c (" ", Hello = 2, World = 2)) Hello World car mpg cyl disp hp Mazda RX4 21.0 6160110 Mazda RX4 Wag 21.0 6160110 Datsun 710 22.8 410893 Hornet 4 Drive 21.4 6258110 Hornet Sportabout 18.7 8360175 gt L’extension gt en cours de développement par l’équipe de RStudio vise, un peu sur le modèle de ggplot2, à proposer une grammaire des tableaux . L’extension n’est pour le moment compatible qu’avec un rendu HTML mais la prise en compte du format PDF est prévue. C’est une extension encore assez nouvelle mais qui risque de se développer assez rapidement vu les équipes en charge de son développement. library (gt) Attaching package: 'gt' The following object is masked from 'package:formattable':

currency exibble %>% gt (rowname_col = "row", groupname_col = "group") %>% tab_spanner (label = "Date et/ou Heure", columns = vars (date, time, datetime)) %>% fmt_number (columns = vars (num), decimals = 2, locale = "fr") %>% fmt_date (columns = vars (date), date_style = 7) %>% fmt_time (columns = vars (time), time_style = 4) %>% fmt_datetime (columns = vars (datetime), date_style = 6, time_style = 4) %>% fmt_currency (columns = vars (currency), currency = "EUR", locale = "fr") %>% tab_options (column_labels.font.size = "small", table.font.size = "small", row_group.font.size = "small", data_row.padding = px (3)) %>% summary_rows (groups = TRUE, columns = vars (num), fns = list (moyenne = ~ mean (., na.rm = TRUE), total = ~ sum (., na.rm = TRUE))) %>% grand_summary_rows (columns = vars (currency), fns = list (` grand total ` = ~ sum (., na.rm = TRUE)), formatter = fmt_currency, currency = "EUR") html {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#jhkzkhgvpt .gt_table {
display: table;
border-collapse: collapse;
margin-left: auto;
margin-right: auto;
color: #333333;
font-size: small;
background-color: #FFFFFF;
width: auto;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #A8A8A8;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #A8A8A8;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
}

#jhkzkhgvpt .gt_heading {
background-color: #FFFFFF;
text-align: center;
border-bottom-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}

#jhkzkhgvpt .gt_title {
color: #333333;
font-size: 125%;
font-weight: initial;
padding-top: 4px;
padding-bottom: 4px;
border-bottom-color: #FFFFFF;
border-bottom-width: 0;
}

#jhkzkhgvpt .gt_subtitle {
color: #333333;
font-size: 85%;
font-weight: initial;
padding-top: 0;
padding-bottom: 4px;
border-top-color: #FFFFFF;
border-top-width: 0;
}

#jhkzkhgvpt .gt_bottom_border {
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}

#jhkzkhgvpt .gt_col_headings {
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}

#jhkzkhgvpt .gt_col_heading {
color: #333333;
background-color: #FFFFFF;
font-size: small;
font-weight: normal;
text-transform: inherit;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 6px;
padding-left: 5px;
padding-right: 5px;
overflow-x: hidden;
}

#jhkzkhgvpt .gt_column_spanner_outer {
color: #333333;
background-color: #FFFFFF;
font-size: small;
font-weight: normal;
text-transform: inherit;
padding-top: 0;
padding-bottom: 0;
padding-left: 4px;
padding-right: 4px;
}

#jhkzkhgvpt .gt_column_spanner_outer:first-child {
padding-left: 0;
}

#jhkzkhgvpt .gt_column_spanner_outer:last-child {
padding-right: 0;
}

#jhkzkhgvpt .gt_column_spanner {
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 6px;
overflow-x: hidden;
display: inline-block;
width: 100%;
}

#jhkzkhgvpt .gt_group_heading {
padding: 8px;
color: #333333;
background-color: #FFFFFF;
font-size: small;
font-weight: initial;
text-transform: inherit;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
}

#jhkzkhgvpt .gt_empty_group_heading {
padding: 0.5px;
color: #333333;
background-color: #FFFFFF;
font-size: small;
font-weight: initial;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: middle;
}

#jhkzkhgvpt .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
}

#jhkzkhgvpt .gt_from_md > :first-child {
margin-top: 0;
}

#jhkzkhgvpt .gt_from_md > :last-child {
margin-bottom: 0;
}

#jhkzkhgvpt .gt_row {
padding-top: 3px;
padding-bottom: 3px;
padding-left: 5px;
padding-right: 5px;
margin: 10px;
border-top-style: solid;
border-top-width: 1px;
border-top-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
overflow-x: hidden;
}

#jhkzkhgvpt .gt_stub {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: solid;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 12px;
}

#jhkzkhgvpt .gt_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}

#jhkzkhgvpt .gt_first_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
}

#jhkzkhgvpt .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}

#jhkzkhgvpt .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: double;
border-top-width: 6px;
border-top-color: #D3D3D3;
}

#jhkzkhgvpt .gt_table_body {
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}

#jhkzkhgvpt .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}

#jhkzkhgvpt .gt_footnote {
margin: 0px;
font-size: 90%;
padding: 4px;
}

#jhkzkhgvpt .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}

#jhkzkhgvpt .gt_sourcenote {
font-size: 90%;
padding: 4px;
}

#jhkzkhgvpt .gt_left {
text-align: left;
}

#jhkzkhgvpt .gt_center {
text-align: center;
}

#jhkzkhgvpt .gt_right {
text-align: right;
font-variant-numeric: tabular-nums;
}

#jhkzkhgvpt .gt_font_normal {
font-weight: normal;
}

#jhkzkhgvpt .gt_font_bold {
font-weight: bold;
}

#jhkzkhgvpt .gt_font_italic {
font-style: italic;
}

#jhkzkhgvpt .gt_super {
font-size: 65%;
}

#jhkzkhgvpt .gt_footnote_marks {
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} num char fctr Date et/ou Heure currency date time datetime grp_a row_1 0,11 apricot one 15 janv. 2015 1:35 janv. 1, 2018 2:22 €49,95 row_2 2,22 banana two 15 févr. 2015 2:40 févr. 2, 2018 2:33 €17,95 row_3 33,33 coconut three 15 mars 2015 3:45 mars 3, 2018 3:44 €1,39 row_4 444,40 durian four 15 avr. 2015 4:50 avr. 4, 2018 3:55 €65 100,00 moyenne 120.02 — — — — — — total 480.06 — — — — — — grp_b row_5 5 550,00 NA five 15 mai 2015 5:55 mai 5, 2018 4:00 €1 325,81 row_6 NA fig six 15 juin 2015 NA juin 6, 2018 4:11 €13,26 row_7 777 000,00 grapefruit seven NA 7:10 juil. 7, 2018 5:22 NA row_8 8 880 000,00 honeydew eight 15 août 2015 8:20 NA €0,44 moyenne 3,220,850.00 — — — — — — total 9,662,550.00 — — — — — — grand total — — — — — — €66,508.79 Pour en savoir plus : https://gt.rstudio.com/ Autres extensions On peut également citer les extensions suivantes : flextable, assez complet (https://davidgohel.github.io/flextable) pander xtable tables sjPlot et ses fonctions sjt* Pour chacune, vous trouverez une documentation sur leur page CRAN, notamment sous forme de vignettes. Modèles de documents On a vu ici la production de documents classiques, mais R Markdown permet de créer bien d’autres choses. Le site de documentation de l’extension propose une galerie des différentes sorties possibles. On peut ainsi créer des slides, des sites Web ou même des livres entiers, comme le présent document. Slides Un usage intéressant est la création de diaporamas pour des présentations sous forme de slides. Le principe reste toujours le même : on mélange texte au format Markdown et code R, et R Markdown transforme le tout en présentations au format HTML ou PDF . En général les différents slides sont séparés au niveau de certains niveaux de titre. Certains modèles de slides sont inclus avec R Markdown, notamment : ioslides et Slidy pour des présentations HTML beamer pour des présentations en PDF via LaTeX Quand vous créez un nouveau document dans RStudio, ces modèles sont accessibles via l’entrée Presentation : Créer une présentation R Markdown D’autres extensions, qui doivent être installées séparément, permettent aussi des diaporamas dans des formats variés. On citera notamment : revealjs (https://github.com/rstudio/revealjs) pour des présentations HTLM basées sur le framework reveal.js rmdshower (https://github.com/mangothecat/rmdshower) pour des diaporamas HTML basés sur shower Une fois l’extension installée, elle propose en général un template de départ lorsqu’on crée un nouveau document dans RStudio. Ceux-ci sont accessibles depuis l’entrée From Template . Créer une présentation à partir d’un template Templates Il existe également différents templates permettant de changer le format et la présentation des documents générés. Une liste de ces formats et leur documentation associée est accessible depuis la page formats de la documentation. On notera notamment : le template distill comportant de nombreuses options pour des documents scientifiques et techniques au format HTML (plus d’infos sur https://rstudio.github.io/distill/) ; des formats d’article correspondant à des publications dans différentes revues : jss_article, elsevier_article, etc. ; le format Tufte Handouts qui permet de produire des documents PDF ou HTML dans un format proche de celui utilisé par Edward Tufte pour certaines de ses publications. Enfin, l’extension rmdformats (https://github.com/juba/rmdformats) de Julien Barnier propose plusieurs modèles HTML adaptés notamment pour des documents longs : Modèle readthedown Modèle html_clean Modèle material Là encore, la plupart du temps, ces modèles de documents proposent un template de départ lorsqu’on crée un nouveau document dans RStudio (entrée From Template) : Créer un document à partir d’un template Ressources Les ressources suivantes sont toutes en anglais\dots Yihui Xie, J. J. Allaire et Garrett Grolemund ont écrit un livre dédié intitulé R Markdown: The Definitive Guide . Ce livre est intégralement accessible à https://bookdown.org/yihui/rmarkdown/ . L’ouvrage R for data science, accessible en ligne, contient un chapitre dédié à R Markdown . Le site officiel de l’extension contient une documentation très complète, tant pour les débutants que pour un usage avancé. Enfin, l’aide de RStudio (menu Help puis Cheatsheets) permet d’accéder à deux documents de synthèse : une “antisèche” synthétique (R Markdown Cheat Sheet) et un “guide de référence” plus complet (R Markdown Reference Guide). On peut citer les formats odt, rtf, Markdown, etc. Si vous n’utilisez pas ce dernier, l’extension peut être installée à part avec install.packages("rmarkdown") et chargée explicitement avec library(rmarkdown) . Il est possible d’inclure dans un document R Markdown des blocs de code d’autres langages Ce comportement peut être modifié en cliquant sur l’icône d’engrenage de la barre d’outils et en choisissant Chunk Output in Console Mettre en forme des nombres Ce chapitre est en cours d’écriture. Le plus simple est d’avoir recours à l’extension scales et aux fonctions number, comma, percent, unit, ordinal et pvalue . Couleurs et Palettes Noms de couleur Couleurs RVB Palettes de couleurs Palettes natives Color Brewer Viridis ggsci hrbrthemes ggthemes khroma paletteer Noms de couleur Lorsque l’on doit indiquer à R une couleur, notamment dans les fonctions graphiques, on peut mentionner certaines couleurs en toutes lettres (en anglais) comme "red" ou "blue" . La liste des couleurs reconnues par R est disponible sur http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf . Couleurs RVB En informatique, les couleurs sont usuellement codées en Rouge/Vert/Bleu (voir https://fr.wikipedia.org/wiki/Rouge_vert_bleu) et représentées par un code hexadécimal à 6 caractères, précédés du symbole # . Ce code est reconnu par R et on pourra par exemple indiquer "#FF0000" pour la couleur rouge. Le code hexadécimal des différentes couleurs peut s’obtenir aisément sur internet, de nombreux sites étant consacrés aux palettes de couleurs. Parfois, au lieu du code hexadécimal, les couleurs RVB sont indiquées avec trois chiffres entiers compris entre 0 et 255. La conversion en hexadécimal se fait avec la fonction rgb . rgb (255, 0, 0, maxColorValue = 255) [1] "#FF0000" Palettes de couleurs Palettes natives R fournit nativement quelques palettes de couleurs continues telles que rainbow, heat.colors, terrain.colors, topo.colors ou encore cm.colors . Voici un aperçu de ces palettes de couleurs : Color Brewer Le projet Color Brewer a développé des palettes cartographiques, à la fois séquentielles, divergentes et catégorielles, présentées en détail sur http://colorbrewer2.org/ . Pour chaque type de palette, et en fonction du nombre de classes, est indiqué sur ce site si la palette est adaptée aux personnes souffrant de daltonisme, si elle est rendra correctement sur écran, en cas d’impression couleur et en cas d’impression en noir et blanc. Voici un aperçu des différentes palettes disponibles : L’extension RColorBrewer permets d’accéder à ces palettes sous R . Si on utilise ggplot2, les palettes Color Brewer sont directement disponibles via les fonctions scale_fill_brewer et scale_colour_brewer . ATTENTION : les palettes Color Brewer sont seulement implémentées pour des variables catégorielles. Il est cependant possible de les utiliser avec des variables continues en les combinants avec scale_fill_gradientn ou scale_coulour_gradientn (en remplaçant "Set1" par le nom de la palette désirée) : scale_fill_gradientn (values = RColorBrewer :: brewer.pal (6, "Set1")) Viridis Les palettes de couleurs de la famille Viridis ont initialement été créées pour Matplolib de telles manières que : les couleurs sont perçues de manière uniformes, même lorsqu’elles sont imprimées en noir et blanc ; les couleurs sont distinguées par les formes les plus courantes de daltonisme. Voici un aperçu des différentes palettes de cette famille : Ces palettes sont accessibles de manière générale via l’extension viridis . Elles sont également implémentées dans gpplot2 via les fonctions scale_fill_viridis_c et scale_colour_viridis_c pour des variables continues et scale_fill_viridis_d et scale_colour_viridis_d pour des variables discrètes. ggsci L’extension ggsci fournie plusieurs palettes de couleurs inspirées de journaux scientifiques (comme le Lancet ou le New England Journal of Medicine) mais aussi de séries/films (comme les Simpsons ou Star Trek). On y trouve également des palettes continues basées sur Material Design de Google . Toutes ces palettes sont directement utilisables avec ggplot2 . Plus d’informations sur https://cran.r-project.org/web/packages/ggsci/vignettes/ggsci.html . hrbrthemes L’extension hrbrthemes propose deux palettes discrètes : ipsum_pal et ft_pal ainsi que les fonctions correspondantes pour ggplot2 . library (hrbrthemes) scales :: show_col (ipsum_pal ()(9)) scales :: show_col (ft_pal ()(9)) ggthemes L’extension ggthemes fournit plusieurs palettes de couleurs, en particulier few_pal issue de l’ouvrage Show Me the Numbers (2012) de Stephen Few ; colorblind_pal adaptées aux personnes daltoniennes ; ptol_pal inspirée de l’ouvrage Colour Schemes (2012) de Paul Tol ; et diverses autres (voir https://jrnold.github.io/ggthemes/). library (ggthemes) scales :: show_col (few_pal ()(8)) scales :: show_col (few_pal ("Dark")(8)) scales :: show_col (colorblind_pal ()(8)) scales :: show_col (ptol_pal ()(12)) khroma L’extension khroma implémente elle aussi les palettes de couleurs proposées par Paul Tol dans Colour Schemes (2012) mais de manière plus complète que ggthemes . library (khroma) plot_scheme (colour ("bright")(7), colours = TRUE) library (ggplot2) p <- ggplot (mpg) + aes (x = displ, y = hwy, colour = class) + geom_point () p + scale_colour_bright () plot_scheme (colour ("vibrant")(7), colours = TRUE) p + scale_colour_vibrant () plot_scheme (colour ("muted")(9), colours = TRUE) p + scale_colour_muted () Pour plus de détails, voir https://khroma.archaeo.science/articles/tol.html paletteer L’extension paletteer vise à proposer une interface unifiée pour l’utilisation de palettes de couleurs fournies par d’autres extensions. scale_color_paletteer_d et scale_color_paletteer_c permettent de sélectionner une des palettes disponibles avec un usage avec ggplot2 . Il existe bien entendu leur équivalent pour l’esthétique fill . library (paletteer) ggplot (iris) + aes (Sepal.Length, Sepal.Width, color = Species) + geom_point () + scale_color_paletteer_d ("nord::aurora") Voici une liste de palettes disponibles via paletteer : Attaching package: 'gt' The following object is masked from 'package:highcharter':

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} Package and Palette Name Color Count and Palette awtools a_palette 8 bpalette 16 gpalette 4 mpalette 9 spalette 6 dichromat BrowntoBlue.10 10 BrowntoBlue.12 12 BluetoDarkOrange.12 12 BluetoDarkOrange.18 18 DarkRedtoBlue.12 12 DarkRedtoBlue.18 18 BluetoGreen.14 14 BluetoGray.8 8 BluetoOrangeRed.14 14 BluetoOrange.10 10 BluetoOrange.12 12 BluetoOrange.8 8 LightBluetoDarkBlue.10 10 LightBluetoDarkBlue.7 7 Categorical.12 12 GreentoMagenta.16 16 SteppedSequential.5 25 dutchmasters milkmaid 13 pearl_earring 11 view_of_Delft 12 little_street 11 anatomy 7 staalmeesters 7 ggsci nrc_npg 10 default_aaas 10 default_nejm 8 lanonc_lancet 9 default_jama 7 default_jco 10 default_ucscgb 26 category10_d3 10 category20_d3 20 category20b_d3 20 category20c_d3 20 default_igv 51 alternating_igv 2 default_locuszoom 7 default_uchicago 9 light_uchicago 9 dark_uchicago 9 hallmarks_dark_cosmic 10 hallmarks_light_cosmic 10 signature_substitutions_cosmic 6 springfield_simpsons 16 planetexpress_futurama 12 schwifty_rickandmorty 12 uniform_startrek 7 legacy_tron 7 default_gsea 12 red_material 10 pink_material 10 purple_material 10 deep-purple_material 10 indigo_material 10 blue_material 10 light-blue_material 10 cyan_material 10 teal_material 10 green_material 10 light-green_material 10 lime_material 10 yellow_material 10 amber_material 10 orange_material 10 deep-orange_material 10 brown_material 10 grey_material 10 blue-grey_material 10 ggpomological pomological_base 7 pomological_palette 9 ggthemes calc 12 manyeys 19 gdoc 10 fivethirtyeight 6 colorblind 8 Tableau 10 10 Tableau 20 20 Color Blind 10 Seattle Grays 5 Traffic 9 Miller Stone 11 Superfishel Stone 10 Nuriel Stone 9 Jewel Bright 9 Summer 8 Winter 10 Green-Orange-Teal 12 Red-Blue-Brown 12 Purple-Pink-Gray 12 Hue Circle 19 Classic 10 10 Classic 10 Medium 10 Classic 10 Light 10 Classic 20 20 Classic Gray 5 5 Classic Color Blind 10 Classic Traffic Light 9 Classic Purple-Gray 6 6 Classic Purple-Gray 12 12 Classic Green-Orange 6 6 Classic Green-Orange 12 12 Classic Blue-Red 6 6 Classic Blue-Red 12 12 Classic Cyclic 13 few_Light 9 few_Medium 9 few_Dark 9 excel_Atlas 6 excel_Badge 6 excel_Berlin 6 excel_Celestial 6 excel_Crop 6 excel_Depth 6 excel_Droplet 6 excel_Facet 6 excel_Feathered 6 excel_Gallery 6 excel_Headlines 6 excel_Integral 6 excel_Ion_Boardroom 6 excel_Ion 6 excel_Madison 6 excel_Main_Event 6 excel_Mesh 6 excel_Office_Theme 6 excel_Organic 6 excel_Parallax 6 excel_Parcel 6 excel_Retrospect 6 excel_Savon 6 excel_Slice 6 excel_Vapor_Trail 6 excel_View 6 excel_Wisp 6 excel_Wood_Type 6 excel_Aspect 6 excel_Blue_Green 6 excel_Blue_II 6 excel_Blue_Warm 6 excel_Blue 6 excel_Grayscale 6 excel_Green_Yellow 6 excel_Green 6 excel_Marquee 6 excel_Median 6 excel_Office_2007-2010 6 excel_Orange_Red 6 excel_Orange 6 excel_Paper 6 excel_Red_Orange 6 excel_Red_Violet 6 excel_Red 6 excel_Slipstream 6 excel_Violet_II 6 excel_Violet 6 excel_Yellow_Orange 6 excel_Yellow 6 wsj_rgby 4 wsj_red_green 2 wsj_black_green 4 wsj_dem_rep 3 wsj_colors6 6 stata_s2color 15 stata_s1rcolor 15 stata_s1color 15 stata_mono 15 stata_economist 15 hc_default 10 hc_darkunica 11 hc_bg 5 hc_fg 12 ghibli MarnieLight1 7 MarnieMedium1 7 MarnieDark1 7 MarnieLight2 7 MarnieMedium2 7 MarnieDark2 7 PonyoLight 7 PonyoMedium 7 PonyoDark 7 LaputaLight 7 LaputaMedium 7 LaputaDark 7 MononokeLight 7 MononokeMedium 7 MononokeDark 7 SpiritedLight 7 SpiritedMedium 7 SpiritedDark 7 YesterdayLight 7 YesterdayMedium 7 YesterdayDark 7 KikiLight 7 KikiMedium 7 KikiDark 7 TotoroLight 7 TotoroMedium 7 TotoroDark 7 grDevices blues9 9 jcolors default 5 pal2 5 pal3 5 pal4 6 pal5 6 pal6 8 pal7 8 pal8 12 pal9 6 pal10 10 pal11 12 pal12 13 rainbow 9 LaCroixColoR PassionFruit 6 Mango 6 Pure 6 Lime 6 Lemon 6 Orange 6 Berry 6 CranRaspberry 6 Pamplemousse 6 PeachPear 6 Coconut 6 Apricot 6 Tangerine 7 KeyLime 5 PommeBaya 5 CeriseLimon 6 PinaFraise 6 KiwiSandia 7 MelonPomelo 6 MurePepino 6 paired 14 NineteenEightyR cobra 6 electronic_night 5 hotpink 5 malibu 5 miami1 5 miami2 5 seventies_aint_done_yet 5 sonny 5 sunset1 5 sunset2 5 sunset3 5 youngturqs 12 nord polarnight 4 snowstorm 3 frost 4 aurora 5 lumina 5 mountain_forms 5 silver_mine 5 lake_superior 6 victory_bonds 5 halifax_harbor 6 moose_pond 8 algoma_forest 7 rocky_mountain 6 red_mountain 8 baie_mouton 7 afternoon_prarie 9 ochRe namatjira_qual 8 namatjira_div 8 mccrea 11 parliament 8 tasmania 7 nolan_ned 5 winmar 7 olsen_qual 6 olsen_seq 14 williams_pilbara 7 healthy_reef 9 emu_woman_paired 18 galah 6 lorikeet 6 dead_reef 6 jumping_frog 5 palettetown bulbasaur 13 ivysaur 13 venusaur 14 charmander 13 charmeleon 12 charizard 14 squirtle 14 wartortle 13 blastoise 14 caterpie 12 metapod 5 butterfree 13 weedle 11 kakuna 8 beedrill 14 pidgey 12 pidgeotto 13 pidgeot 14 rattata 13 raticate 13 spearow 15 fearow 12 ekans 12 arbok 10 pikachu 10 raichu 14 sandshrew 10 sandslash 13 nidoranf 11 nidorina 13 nidoqueen 13 nidoranm 10 nidorino 12 nidoking 11 clefairy 13 clefable 12 vulpix 13 ninetales 7 jigglypuff 11 wigglytuff 12 zubat 11 golbat 11 oddish 10 gloom 14 vileplume 13 paras 12 parasect 11 venonat 14 venomoth 11 diglett 4 dugtrio 12 meowth 13 persian 12 psyduck 10 golduck 13 mankey 14 primeape 15 growlithe 11 arcanine 14 poliwag 11 poliwhirl 9 poliwrath 9 abra 8 kadabra 14 alakazam 12 machop 13 machoke 13 machamp 13 bellsprout 13 weepinbell 14 victreebel 14 tentacool 14 tentacruel 14 geodude 6 graveler 6 golem 12 ponyta 11 rapidash 11 slowpoke 12 slowbro 13 magnemite 14 magneton 14 farfetch'd 14 doduo 9 dodrio 15 seel 11 dewgong 7 grimer 9 muk 8 shellder 9 cloyster 9 gastly 8 haunter 10 gengar 10 onix 6 drowzee 9 hypno 9 krabby 10 kingler 10 voltorb 9 electrode 10 exeggcute 9 exeggutor 14 cubone 13 marowak 14 hitmonlee 12 hitmonchan 13 lickitung 9 koffing 12 weezing 13 rhyhorn 8 rhydon 11 chansey 9 tangela 9 kangaskhan 14 horsea 10 seadra 10 goldeen 11 seaking 12 staryu 12 starmie 12 mr. mime 12 scyther 12 jynx 15 electabuzz 10 magmar 12 pinsir 9 tauros 13 magikarp 13 gyarados 15 lapras 15 ditto 6 eevee 9 vaporeon 15 jolteon 11 flareon 10 porygon 11 omanyte 12 omastar 12 kabuto 11 kabutops 9 aerodactyl 7 snorlax 14 articuno 12 zapdos 10 moltres 14 dratini 9 dragonair 12 dragonite 13 mewtwo 9 mew 6 chikorita 11 bayleef 14 meganium 14 cyndaquil 13 quilava 14 typhlosion 14 totodile 11 croconaw 13 feraligatr 14 sentret 12 furret 11 hoothoot 13 noctowl 13 ledyba 12 ledian 14 spinarak 14 ariados 14 crobat 9 chinchou 13 lanturn 14 pichu 11 cleffa 11 igglybuff 9 togepi 12 togetic 11 natu 14 xatu 15 mareep 14 flaaffy 13 ampharos 12 bellossom 13 marill 10 azumarill 10 sudowoodo 11 politoed 14 hoppip 10 skiploom 11 jumpluff 13 aipom 12 sunkern 13 sunflora 12 yanma 11 wooper 9 quagsire 9 espeon 9 umbreon 10 murkrow 12 slowking 13 misdreavus 13 unown 5 wobbuffet 8 girafarig 13 pineco 5 forretress 10 dunsparce 13 gligar 12 steelix 7 snubbull 12 granbull 11 qwilfish 12 scizor 11 shuckle 12 heracross 11 sneasel 14 teddiursa 12 ursaring 13 slugma 9 magcargo 12 swinub 9 piloswine 12 corsola 9 remoraid 9 octillery 12 delibird 13 mantine 13 skarmory 12 houndour 11 houndoom 12 kingdra 12 phanpy 9 donphan 13 porygon2 12 stantler 12 smeargle 12 tyrogue 12 hitmontop 12 smoochum 12 elekid 9 magby 10 miltank 15 blissey 9 raikou 13 entei 14 suicune 14 larvitar 9 pupitar 9 tyranitar 11 lugia 9 ho-oh 13 celebi 11 treecko 14 grovyle 14 sceptile 13 torchic 12 combusken 14 blaziken 14 mudkip 14 marshtomp 14 swampert 14 poochyena 14 mightyena 11 zigzagoon 14 linoone 11 wurmple 13 silcoon 9 beautifly 13 cascoon 8 dustox 14 lotad 13 lombre 12 ludicolo 15 seedot 11 nuzleaf 14 shiftry 13 taillow 15 swellow 15 wingull 14 pelipper 15 ralts 11 kirlia 13 gardevoir 12 surskit 12 masquerain 14 shroomish 11 breloom 15 slakoth 14 vigoroth 15 slaking 14 nincada 15 ninjask 13 shedinja 10 whismur 12 loudred 14 exploud 15 makuhita 12 hariyama 15 azurill 11 nosepass 11 skitty 11 delcatty 15 sableye 13 mawile 14 aron 9 lairon 12 aggron 12 meditite 13 medicham 13 electrike 10 manectric 13 plusle 10 minun 13 volbeat 15 illumise 15 roselia 15 gulpin 11 swalot 14 carvanha 13 sharpedo 14 wailmer 15 wailord 13 numel 15 camerupt 13 torkoal 12 spoink 13 grumpig 15 spinda 10 trapinch 10 vibrava 14 flygon 15 cacnea 14 cacturne 12 swablu 12 altaria 12 zangoose 15 seviper 14 lunatone 14 solrock 14 barboach 12 whiscash 15 corphish 13 crawdaunt 15 baltoy 7 claydol 12 lileep 12 cradily 12 anorith 14 armaldo 14 feebas 13 milotic 14 castform 9 kecleon 15 shuppet 12 banette 12 duskull 13 dusclops 11 tropius 15 chimecho 13 absol 11 wynaut 11 snorunt 15 glalie 13 spheal 12 sealeo 15 walrein 12 clamperl 14 huntail 14 gorebyss 12 relicanth 13 luvdisc 9 bagon 11 shelgon 12 salamence 13 beldum 10 metang 13 metagross 13 regirock 13 regice 10 registeel 13 latias 14 latios 14 kyogre 14 groudon 14 rayquaza 12 jirachi 12 deoxys 14 teamrocket 6 starterspairs 6 startersDark 6 pals alphabet 26 alphabet2 26 glasbey 32 kelly 22 polychrome 36 stepped 24 tol 12 watlington 16 Polychrome alphabet 26 dark 24 glasbey 32 green.armytage 26 kelly 22 light 24 palette36 36 rcartocolor ag_Sunset 7 ag_GrnYl 7 Tropic 7 Temps 7 TealRose 7 Geyser 7 Fall 7 Earth 7 ArmyRose 7 Vivid 12 Safe 12 Prism 12 Pastel 12 Bold 12 Antique 12 TealGrn 7 Teal 7 SunsetDark 7 Sunset 7 RedOr 7 PurpOr 7 Purp 7 PinkYl 7 Peach 7 OrYel 7 Mint 7 Magenta 7 Emrld 7 DarkMint 7 BurgYl 7 Burg 7 BrwnYl 7 BluYl 7 BluGrn 7 RColorBrewer BrBG 11 PiYG 11 PRGn 11 PuOr 11 RdBu 11 RdGy 11 RdYlBu 11 RdYlGn 11 Spectral 11 Accent 8 Dark2 8 Paired 12 Pastel1 9 Pastel2 8 Set1 9 Set2 8 Set3 12 Blues 9 BuGn 9 BuPu 9 GnBu 9 Greens 9 Greys 9 Oranges 9 OrRd 9 PuBu 9 PuBuGn 9 PuRd 9 Purples 9 RdPu 9 Reds 9 YlGn 9 YlGnBu 9 YlOrBr 9 YlOrRd 9 Redmonder dPBIYlBu 11 dPBIYlPu 11 dPBIPuGn 11 dPBIPuOr 11 dPBIRdBu 11 dPBIRdGy 11 dPBIRdGn 11 qMSOStd 10 qMSO12 8 qMSO15 8 qMSOBuWarm 8 qMSOBu 8 qMSOBu2 8 qMSOBuGn 8 qMSOGn 8 qMSOGnYl 8 qMSOYl 8 qMSOYlOr 8 qMSOOr 8 qMSOOrRd 8 qMSORdOr 8 qMSORd 8 qMSORdPu 8 qMSOPu 8 qMSOPu2 8 qMSOMed 8 qMSOPap 8 qMSOMrq 8 qMSOSlp 8 qMSOAsp 8 qPBI 8 sPBIGn 9 sPBIGy1 9 sPBIRd 9 sPBIYl 9 sPBIGy2 9 sPBIBu 9 sPBIOr 9 sPBIPu 9 sPBIYlGn 9 sPBIRdPu 9 tidyquant tq_light 12 tq_dark 12 tq_green 12 wesanderson BottleRocket1 7 BottleRocket2 5 Rushmore1 5 Rushmore 5 Royal1 4 Royal2 5 Zissou1 5 Darjeeling1 5 Darjeeling2 5 Chevalier1 4 FantasticFox1 5 Moonrise1 4 Moonrise2 4 Moonrise3 5 Cavalcanti1 5 GrandBudapest1 4 GrandBudapest2 4 IsleofDogs1 6 IsleofDogs2 5 yarrr basel 10 pony 9 xmen 8 decision 6 southpark 6 google 4 eternal 7 evildead 6 usualsuspects 7 ohbrother 7 appletv 6 brave 5 bugs 5 cars 5 nemo 5 rat 5 up 5 espresso 5 ipod 7 info 9 info2 14 The paletteer package is maintained by Emil Hvitfeldt. More information can be found on the paletteer site and on the CRAN info page . Annotations mathématiques Combiner texte et expression Intégrer une valeur calculée dans une expression Syntaxe de plotmath Pour ajouter des annotation mathématiques à un graphique, comme une équation, on aura recours à la fonction expression . Les expressions qui peuvent être utilisées sont présentées en détail dans l’aide en ligne de plotmath, visible également sur http://www.rdocumentation.org/packages/grDevices/functions/plotmath . Combiner texte et expression On aura recours à la fonction paste à l’intérieur de l’appel à expression . Un exemple : # données aléatoires df <- data.frame (x = rnorm (100), y = rnorm (100)) library (ggplot2) ggplot (df) + aes (x = x, y = y) + geom_point () + xlab (expression (hat (mu)[0])) + ylab (expression (alpha^beta)) + ggtitle (expression (paste ("Plot of ", alpha^beta, " versus ", hat (mu)[0]))) Intégrer une valeur calculée dans une expression Pour intégrer une valeur pré-calculée, et donc stockée dans un objet R, dans une expression, on aura recours à la fonction substitute . x_mean <- 1.5 x_sd <- 1.2 df <- data.frame (x = rnorm (100, x_mean, x_sd)) ggplot (df) + aes (x = x) + geom_histogram (binwidth = .25) + ggtitle (substitute (paste (X[i], " ~ N(", mu, "=", m, ", ", sigma^2, "=", s2, ")"), list (m = x_mean, s2 = x_sd^2))) Syntaxe de plotmath demo (plotmath) Calculer un âge Rappel sur les âges Le package lubridate Calcul d’un âge exact Cas particulier des personnes nées un 29 février Âge révolu ou âge au dernier anniversaire Âge par différence de millésimes Calcul d’un âge moyen Notes La version originale de cette astuce a été publiée par Joseph Larmarange sur http://joseph.larmarange.net/?Calculer-proprement-un-age-sous-R . Le calcul d’un âge sous R n’est pas forcément aussi trivial qu’il n’y parait. Rappel sur les âges Il convient en premier lieu de rappeler les principaux âges utilisés les démographes : L’âge - on précise parfois âge chronologique - est une des caractéristiques fondamentales de la structure des populations. On l’exprime généralement en années, ou en années et mois, voire en mois et jours, pour les enfants en bas âge ; parfois en années et fractions décimales d’année. Les démographes arrondissent d’ordinaire l’âge à l’unité inférieure, l’exprimant ainsi en années révolues, ou années accomplies, le cas échéant en mois révolus, ou mois accomplis. Cet âge est aussi l’âge au dernier anniversaire. On trouve aussi, dans les statistiques, l’âge atteint dans l’année, qui est égal à la différence de millésimes entre l’année considérée et l’année de naissance. [\dots] On est parfois conduit à préciser que l’on considère un âge exact, pour éviter toute confusion avec un âge en années révolues, qui représente en fait une classe d’âges exacts. Source : Demopædia (322) Le package lubridate Le package lubridate est spécialement développé pour faciliter la manipulation et le calcul autour des dates . La version de développement intègre depuis peu une fonction time_length adaptée au calcul des âges exacts. La fonction time_length étant récente, elle n’est pas encore disponible dans la version stable du package. Pour installer la version de développement de lubridate, on aura recours au package devtools : library (devtools) install_github ("tidyverse/lubridate") Nous noterons naiss la date de naissance et evt la date à laquelle nous calculerons l’âge. Calcul d’un âge exact On appelle âge exact l’expression d’un âge avec sa partie décimale. Une approche simple consiste à calculer une différence en jours puis à diviser par 365. Or, le souci c’est que toutes les années n’ont pas le même nombre de jours. Regardons par exemple ce qui se passe si l’on calcule l’âge au 31 décembre 1999 d’une personne née le 1 er janvier 1900. library (lubridate) naiss <- ymd ("1900-01-01") evt <- ymd ("1999-12-31") time_length (interval (naiss, evt), "days") [1] 36523 time_length (interval (naiss, evt), "days") / 365 [1] 100.063 Or, au 31 décembre 1999, cette personne n’a pas encore fêté son centième anniversaire. Le calcul précédent ne prend pas en compte les années bissextiles. Une approche plus correcte serait de considérer que les années durent en moyenne 365,25 jours. time_length (interval (naiss, evt), "days") / 365.25 [1] 99.99452 Si cette approche semble fonctionner avec cet exemple, ce n’est plus le cas dans d’autres situations. evt <- ymd ("1903-01-01") time_length (interval (naiss, evt), "days") / 365.25 [1] 2.997947 Or, à la date du premier janvier 1903, cette personne a bien fêté son troisième anniversaire. Pour calculer proprement un âge en années (ou en mois), il est dès lors nécessaire de prendre en compte la date anniversaire et le fait que la durée de chaque année (ou mois) est variable. C’est justement ce que fait la fonction time_length appliquée à un objet de type Interval . On détermine le dernier et le prochain anniversaire et l’on rajoute, à l’âge atteint au dernier anniversaire, le ratio entre le nombre de jours entre l’événement et le dernier anniversaire par le nombre de jours entre le prochain et le dernier anniversaire. naiss <- ymd ("1900-01-01") evt <- ymd ("1999-12-31") time_length (interval (naiss, evt), "years") [1] 99.99726 evt <- ymd ("1903-01-01") time_length (interval (naiss, evt), "years") [1] 3 evt <- ymd ("1918-11-11") time_length (interval (naiss, evt), "years") [1] 18.86027 Attention, cela n’est valable que si l’on présente à la fonction time_length un objet de type Interval (pour lequel on connait dès lors la date de début et la date de fin). Si l’on passe une durée (objet de type Duration) à la fonction time_length, le calcul s’effectuera alors en prenant en compte la durée moyenne d’une année (plus précisément 365 jours). naiss <- ymd ("1900-01-01") evt <- ymd ("1999-12-31") time_length (interval (naiss, evt), "years") [1] 99.99726 time_length (evt - naiss, "years") [1] 99.99452 time_length (as.duration (interval (naiss, evt)), "years") [1] 99.99452 Cas particulier des personnes nées un 29 février Pour les personnes nées un 29 février, il existe un certain flou concernant leur date d’anniversaire pour les années non bissextiles. Doit-on considérer qu’il s’agit du 28 février ou du 1 er mars ? Au sens strict, on peut considérer que leur anniversaire a lieu entre le 28 février soir à minuit et le 1 er mars à 0 heure du matin, autrement dit que le 28 février ils n’ont pas encore fêté leur anniversaire. C’est la position adoptée par la fonction time_length . naiss <- ymd ("1992-02-29") evt <- ymd ("2014-02-28") time_length (interval (naiss, evt), "years") [1] 21.99726 evt <- ymd ("2014-03-01") time_length (interval (naiss, evt), "years") [1] 22 Cette approche permets également d’être cohérent avec la manière dont les dates sont prises en compte informatiquement. On considère en effet que lorsque seule la date est précisée (sans mention de l’heure), l’heure correspondante est 0:00 . Autrement dit, "2014-03-01" est équivalent à "2014-03-01 00:00:00" . L’approche adoptée permet donc d’être cohérent lorsque l’anniversaire est calculé en tenant compte des heures. naiss <- ymd ("1992-02-29") evt <- ymd_hms ("2014-02-28 23:00:00") time_length (interval (naiss, evt), "years") [1] 21.99989 evt <- ymd_hms ("2014-03-01 00:00:00") time_length (interval (naiss, evt), "years") [1] 22 evt <- ymd_hms ("2014-03-01 01:00:00") time_length (interval (naiss, evt), "years") [1] 22.00011 naiss <- ymd_hms ("1992-02-29 12:00:00") evt <- ymd_hms ("2014-03-01 01:00:00") time_length (interval (naiss, evt), "years") [1] 22.00011 Âge révolu ou âge au dernier anniversaire Une fois que l’on sait calculer un âge exact, le calcul d’un âge révolu ou âge au dernier anniversaire est assez simple. Il suffit de ne garder que la partie entière de l’âge exact (approche conseillée). naiss <- ymd ("1980-01-09") evt <- ymd ("2015-01-01") time_length (interval (naiss, evt), "years") [1] 34.97808 trunc (time_length (interval (naiss, evt), "years")) [1] 34 Une autre approche consiste à convertir l’intervalle en objet de type Period et à ne prendre en compte que les années. as.period (interval (naiss, evt)) [1] "34y 11m 23d 0H 0M 0S" as.period (interval (naiss, evt)) @ year [1] 34 Âge par différence de millésimes L’ âge par différence de millésimes, encore appelé âge atteint dans l’année, s’obtient tout simplement en soustrayant l’année de naissance à l’année de l’événement. naiss <- ymd ("1980-01-09") evt <- ymd ("2015-01-01") year (evt) - year (naiss) [1] 35 Calcul d’un âge moyen Le calcul d’un âge moyen s’effectue normalement à partir d’âges exacts. Il arrive fréquemment que l’on ne dispose dans les données d’enquêtes que de l’âge révolu. Auquel cas, il faut bien penser à rajouter 0,5 au résultat obtenu. En effet, un âge révolu peut être vu comme une classe d’âges exacts : les individus ayant 20 ans révolus ont entre 20 et 21 ans exacts, soit en moyenne 20,5 ans ! Notes L’ensemble des fonctions présentées peuvent être appliquées à des vecteurs et, par conséquent, aux colonnes d’un tableau de données (data.frame). En l’absence du package lubridate, il reste facile de calculer une durée en jours avec les fonctions de base de R : naiss <- as.Date ("1900-01-01") evt <- as.Date ("1999-12-31") evt - naiss Time difference of 36523 days as.integer (evt - naiss) [1] 36523 Diagramme de Lexis Ce chapitre est en cours d’écriture. Pour réaliser des diagrammes de Lexis, voir l’extension LexisPlotR et sa vignette (en anglais) : https://cran.r-project.org/web/packages/LexisPlotR/vignettes/LexisPlotR.html . Index des concepts A B C D E F G H I K L M N O P Q R S T U V W A ACM Analyse des correspondances multiples (ACM) ACP Analyse des correspondances multiples (ACM) AFC Analyse des correspondances multiples (ACM) âge Calculer un âge âge atteint dans l’année Calculer un âge âge au dernier anniversaire Calculer un âge âge exact Calculer un âge AIC Régression logistique binaire, multinomiale et ordinale aide Premier contact aide en ligne Où trouver de l’aide ? Akaike Information Criterion Analyse de survie Régression logistique binaire, multinomiale et ordinale aléatoire, échantillonnage Définir un plan d’échantillonnage complexe aléatoire, effet NA analyse de séquences Analyse de séquences NA analyse de survie Analyse de survie analyse des biographies Analyse de séquences analyse des correspondances multiples Analyse des correspondances multiples (ACM) analyse en composante principale Analyse des correspondances multiples (ACM) analyse factorielle Analyse des correspondances multiples (ACM) analyse factorielle avec données mixtes Analyse des correspondances multiples (ACM) analyse factorielle des correspondances Analyse des correspondances multiples (ACM) ANOVA Régression logistique binaire, multinomiale et ordinale appariement optimal Analyse de séquences arbre de classification Classification ascendante hiérarchique (CAH) argument Premier contact argument nommé Premier contact argument non nommé Premier contact assignation par indexation Listes et Tableaux de données assignation, opérateur Premier contact attribut Import de données autocomplétion Organiser ses fichiers Premier contact Présentation et Philosophie B barres cumulées, diagramme en Graphiques univariés et bivariés avec ggplot2 NA barres cumulées, graphique Statistique bivariée barres, diagramme en Graphiques univariés et bivariés avec ggplot2 bâton, diagramme Statistique univariée bâtons, diagramme en Graphiques univariés et bivariés avec ggplot2 Bayesian Highest Density Intervals Statistique bivariée binaire, régression logistique Régression logistique binaire, multinomiale et ordinale biographie, analyse Analyse de séquences bitmap Export de graphiques boîte à moustache Statistique bivariée boîte à moustaches Graphiques univariés et bivariés avec ggplot2 Statistique univariée booléenne, valeur Vecteurs, indexation et assignation booléenne, variable Régression logistique binaire, multinomiale et ordinale boxplot Graphiques univariés et bivariés avec ggplot2 Statistique bivariée Statistique univariée C CAH Analyse de séquences Classification ascendante hiérarchique (CAH) camembert, graphique Statistique univariée caractères, chaîne Vecteurs, indexation et assignation catégorielle, variable Facteurs et vecteurs labellisés censure à droite Analyse de survie cercle de corrélation Analyse des correspondances multiples (ACM) chaîne de caractères Vecteurs, indexation et assignation character Vecteurs, indexation et assignation chemin relatif Organiser ses fichiers Chi², distance Classification ascendante hiérarchique (CAH) Chi², résidus Comparaisons (moyennes et proportions) NA Chi², test Comparaisons (moyennes et proportions) Données pondérées NA chunk R Markdown : les rapports automatisés classe de valeurs Statistique univariée classe, homogénéité Analyse de séquences classes latentes, modèle mixte NA classification ascendante hiérarchique Analyse de séquences Classification ascendante hiérarchique (CAH) NA classification, arbre Classification ascendante hiérarchique (CAH) Cleveland, diagramme Graphiques univariés et bivariés avec ggplot2 Statistique univariée cluster Définir un plan d’échantillonnage complexe coefficient de contingence de Cramer Comparaisons (moyennes et proportions) coefficient de corrélation Statistique bivariée coefficient, modèle Régression logistique binaire, multinomiale et ordinale coefficients du modèle Modèles à effets aléatoires (modèles mixtes et GEE) colinéarité Multicolinéarité dans la régression coloration syntaxique Premier contact Présentation et Philosophie commentaire Premier travail avec des données comparaison de courbes de survie (test du logrank) Analyse de survie comparaison de médianes, test Comparaisons (moyennes et proportions) comparaison de moyennes Comparaisons (moyennes et proportions) Données pondérées comparaison de proportions, test Comparaisons (moyennes et proportions) comparaison, opérateur Conditions et comparaisons Vecteurs, indexation et assignation Comprehensive R Archive Network Extensions (installation, mise à jour) condition, indexation Listes et Tableaux de données Vecteurs, indexation et assignation confusion, matrice Régression logistique binaire, multinomiale et ordinale console Premier contact corrélation Multicolinéarité dans la régression corrélation, cercle Analyse des correspondances multiples (ACM) corrélation, coefficient Statistique bivariée corrélation, matrice de Graphiques univariés et bivariés avec ggplot2 correspondances, analyse factorielle Analyse des correspondances multiples (ACM) couleur Couleurs et Palettes couleur, palette Analyse des correspondances multiples (ACM) courbe de densité Graphiques univariés et bivariés avec ggplot2 Cox, modèle Analyse de survie Cramer, coefficient de contingence Comparaisons (moyennes et proportions) CRAN Extensions (installation, mise à jour) croisé, tableau Statistique bivariée CSV, fichier Import de données D data frame Listes et Tableaux de données Premier travail avec des données data.frame Import de données date, variable Calculer un âge dendrogramme Classification ascendante hiérarchique (CAH) densité, courbe de Graphiques univariés et bivariés avec ggplot2 densité, estimation locale Graphiques univariés et bivariés avec ggplot2 Statistique bivariée descriptive, statistique Statistique bivariée Statistique univariée design Données pondérées diagramme de Cleveland Graphiques univariés et bivariés avec ggplot2 Statistique univariée diagramme de Lexis Diagramme de Lexis diagramme de Sankey Autres extensions graphiques diagramme en barres Graphiques univariés et bivariés avec ggplot2 diagramme en barres cumulées Graphiques univariés et bivariés avec ggplot2 diagramme en bâtons Graphiques univariés et bivariés avec ggplot2 Statistique univariée diagramme en secteur Statistique univariée distance Classification ascendante hiérarchique (CAH) distance de Gower Classification ascendante hiérarchique (CAH) distance du Chi² Classification ascendante hiérarchique (CAH) distance du Phi² Classification ascendante hiérarchique (CAH) distance, matrice Analyse de séquences distribution Statistique bivariée Statistique univariée donnée labelissée Facteurs et vecteurs labellisés données pondérées Données pondérées données, exporter Export de données Import de données données, tableau Listes et Tableaux de données droite de régression Graphiques univariés et bivariés avec ggplot2 droite, censure Analyse de survie E écart-type Statistique univariée écart interquartile Statistique univariée échantillonnage aléatoire simple Définir un plan d’échantillonnage complexe échantillonnage équiprobable Définir un plan d’échantillonnage complexe échantillonnage par grappes Définir un plan d’échantillonnage complexe échantillonnage stratifié Définir un plan d’échantillonnage complexe échantillonnage, plan Données pondérées éditeur de script Présentation et Philosophie effet aléatoire NA effet d’interaction Effets d’interaction dans un modèle empirical cumulative distribution function Graphiques univariés et bivariés avec ggplot2 Statistique univariée entier Premier travail avec des données entier, nombre Vecteurs, indexation et assignation entropie transversale Analyse de séquences environnement de développement Présentation et Philosophie environnement de travail Premier contact équiprobable, échantillonnage Définir un plan d’échantillonnage complexe erreurs Modèles à effets aléatoires (modèles mixtes et GEE) estimation locale de densité Graphiques univariés et bivariés avec ggplot2 Statistique bivariée estimation par noyau Statistique univariée étendue Statistique univariée étiquette de valeur Facteurs et vecteurs labellisés Import de données étiquette de variable Facteurs et vecteurs labellisés Import de données étiquettes de valeurs Import de données event history analysis Analyse de séquences exact, âge Calculer un âge explicative, variable Régression logistique binaire, multinomiale et ordinale export de graphiques Export de graphiques Statistique univariée exporter des données Export de données Import de données expression régulière Expressions régulières extension Extensions (installation, mise à jour) F facteur Facteurs et vecteurs labellisés Import de données Premier travail avec des données Régression logistique binaire, multinomiale et ordinale facteurs d’inflation de la variance Multicolinéarité dans la régression factor Facteurs et vecteurs labellisés Premier travail avec des données factoriel, plan Analyse des correspondances multiples (ACM) factorielle, analyse Analyse des correspondances multiples (ACM) fichier CSV Import de données fichier de commandes Premier travail avec des données fichier texte Import de données fichiers Shapefile Import de données Fisher, test exact Comparaisons (moyennes et proportions) FIV Multicolinéarité dans la régression fonction Premier contact fonction de répartition empirique Graphiques univariés et bivariés avec ggplot2 Statistique univariée formule Régression logistique binaire, multinomiale et ordinale Statistique bivariée fréquence, tableau Statistique univariée fusion Fusion de tables fusion de tables Fusion de tables G GEE, modèle NA gestionnaire de versions Organiser ses fichiers Gower, distance Classification ascendante hiérarchique (CAH) Gower, indice de similarité Classification ascendante hiérarchique (CAH) grammaire des tableaux R Markdown : les rapports automatisés graphique de pirates Graphiques univariés et bivariés avec ggplot2 Statistique bivariée graphique en mosaïque Graphiques univariés et bivariés avec ggplot2 graphique en violon Graphiques univariés et bivariés avec ggplot2 graphique, export Export de graphiques Statistique univariée grappe, échantillonnage Définir un plan d’échantillonnage complexe H hazard ratio NA hazard ratio (HR) Analyse de survie HDI Statistique bivariée histogramme Comparaisons (moyennes et proportions) Statistique univariée historique des commandes Premier contact homogénéité des classes Analyse de séquences I image bitmap Export de graphiques image matricielle Export de graphiques image vectorielle Export de graphiques indépendance Statistique bivariée indertie, perte relative Classification ascendante hiérarchique (CAH) index plots Analyse de séquences indexation Vecteurs, indexation et assignation indexation bidimensionnelle Listes et Tableaux de données indexation directe Vecteurs, indexation et assignation indexation par condition Listes et Tableaux de données Vecteurs, indexation et assignation indexation par nom Listes et Tableaux de données indexation par position Vecteurs, indexation et assignation indexation, assignation Listes et Tableaux de données indice de similarité Classification ascendante hiérarchique (CAH) indice de similarité de Gower Classification ascendante hiérarchique (CAH) inertie Analyse des correspondances multiples (ACM) inertie, saut Analyse de séquences Classification ascendante hiérarchique (CAH) installation Installation de R et RStudio integer Premier travail avec des données Vecteurs, indexation et assignation interaction Effets d’interaction dans un modèle intercept Modèles à effets aléatoires (modèles mixtes et GEE) interface Premier contact interquartilen écart Statistique univariée intervalle de confiance Comparaisons (moyennes et proportions) Intervalles de confiance intervalle de confiance d’un odds ratio Régression logistique binaire, multinomiale et ordinale intervalle de confiance d’une moyenne Intervalles de confiance intervalle de confiance d’une proportion Données pondérées Intervalles de confiance invite de commande Premier contact K Kaplan-Meier Analyse de survie L labelled data Facteurs et vecteurs labellisés labellisé, vecteur Facteurs et vecteurs labellisés Import de données labellisée, donnée Facteurs et vecteurs labellisés labellisée, variable Import de données latente, modèle mixte à classes latentes NA LCS Analyse de séquences level, factor Premier travail avec des données Lexis, diagramme Diagramme de Lexis libre, logiciel Présentation et Philosophie life course analysis Analyse de séquences linéaire, régression Statistique bivariée liste Listes et Tableaux de données logical Vecteurs, indexation et assignation logiciel libre Présentation et Philosophie logique, opérateur Conditions et comparaisons Vecteurs, indexation et assignation logique, valeur Vecteurs, indexation et assignation logistique, régression Données pondérées Régression logistique binaire, multinomiale et ordinale logrank, test (comparaison de courbes de survie) Analyse de survie loi normale Comparaisons (moyennes et proportions) Longuest Common Subsequence Analyse de séquences M Mann-Whitney, test Comparaisons (moyennes et proportions) manquante, valeur Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Import de données Régression logistique binaire, multinomiale et ordinale Statistique bivariée Vecteurs, indexation et assignation Markdown R Markdown : les rapports automatisés matching, optimal Analyse de séquences matrice de confusion Régression logistique binaire, multinomiale et ordinale matrice de corrélation Graphiques univariés et bivariés avec ggplot2 matrice de distances Classification ascendante hiérarchique (CAH) maximum Statistique univariée médiane Comparaisons (moyennes et proportions) Statistique univariée médiane, test de comparaison Comparaisons (moyennes et proportions) métadonnée Import de données méthode de Ward Classification ascendante hiérarchique (CAH) minimum Statistique univariée mise à jour, R Installation de R et RStudio mixte, modèle Modèles à effets aléatoires (modèles mixtes et GEE) NA mixte, modèle mixte à classes latentes NA mixtes, analyse factorielle avec données Analyse des correspondances multiples (ACM) modalité Statistique univariée modalité de référence Régression logistique binaire, multinomiale et ordinale modalité, facteur Premier travail avec des données modèle à effets aléatoires Modèles à effets aléatoires (modèles mixtes et GEE) modèle de Cox Analyse de survie modèle GEE NA modèle linéaire généralisé Données pondérées Régression logistique binaire, multinomiale et ordinale modèle mixte Modèles à effets aléatoires (modèles mixtes et GEE) NA modèle, coefficients Modèles à effets aléatoires (modèles mixtes et GEE) modèle, résidus Modèles à effets aléatoires (modèles mixtes et GEE) mosaïque, graphique Graphiques univariés et bivariés avec ggplot2 Statistique bivariée moustaches, boîte Graphiques univariés et bivariés avec ggplot2 Statistique bivariée Statistique univariée moyenne Données pondérées Statistique bivariée Statistique univariée moyenne, âge Calculer un âge moyenne, comparaison Comparaisons (moyennes et proportions) Données pondérées moyenne, intervalle de confiance Intervalles de confiance multi-états, modèle de survie NA multicolinéarité Multicolinéarité dans la régression multicolinéarité parfaite Multicolinéarité dans la régression multidimensional scaling Analyse de séquences multinomiale, régression logistique Régression logistique binaire, multinomiale et ordinale NA N NA Premier contact nom, indexation Listes et Tableaux de données Vecteurs, indexation et assignation nombre entier Vecteurs, indexation et assignation nombre réel Vecteurs, indexation et assignation normale, loi Comparaisons (moyennes et proportions) normalité, test de Shapiro-Wilk Comparaisons (moyennes et proportions) notation formule Formules notation scientifique Comparaisons (moyennes et proportions) noyau, estimation Statistique univariée nuage de points Graphiques univariés et bivariés avec ggplot2 numeric Premier travail avec des données Vecteurs, indexation et assignation numérique, variable Premier travail avec des données Statistique univariée O observation Premier travail avec des données observations répétées, modèle NA odds ratio Comparaisons (moyennes et proportions) Régression logistique binaire, multinomiale et ordinale odds ratio, intervalle de confiance Régression logistique binaire, multinomiale et ordinale opérateur de comparaison Conditions et comparaisons opérateur logique Conditions et comparaisons Vecteurs, indexation et assignation optimal matching Analyse de séquences optimal, appariement Analyse de séquences ordinaire, régression logistique Régression logistique binaire, multinomiale et ordinale ordinale, régression logistique Régression logistique binaire, multinomiale et ordinale NA ordonner le tri à plat Statistique univariée P package Extensions (installation, mise à jour) palette de couleurs Analyse des correspondances multiples (ACM) PAM NA partition Analyse de séquences Classification ascendante hiérarchique (CAH) Partitioning Around Medoids NA pas à pas, sélection descendante Régression logistique binaire, multinomiale et ordinale patron Classification ascendante hiérarchique (CAH) perte relative d’inertie Classification ascendante hiérarchique (CAH) Phi², distance Classification ascendante hiérarchique (CAH) pirate, graphique Graphiques univariés et bivariés avec ggplot2 Statistique bivariée pirateplot Graphiques univariés et bivariés avec ggplot2 Statistique bivariée plan d’échantillonnage Données pondérées plan factoriel Analyse des correspondances multiples (ACM) poids de réplication Régression logistique binaire, multinomiale et ordinale points, nuage de Graphiques univariés et bivariés avec ggplot2 pondération Données pondérées pooled variance Comparaisons (moyennes et proportions) position, indexation Listes et Tableaux de données Vecteurs, indexation et assignation projets Organiser ses fichiers prompt Premier contact proportion, intervalle de confiance Données pondérées Intervalles de confiance proportion, test de comparaison Comparaisons (moyennes et proportions) proxy Installation de R et RStudio Q qualitative, variable Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Régression logistique binaire, multinomiale et ordinale Statistique bivariée Statistique univariée quantile Données pondérées Statistique univariée quantitative, variable Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Régression logistique binaire, multinomiale et ordinale Statistique bivariée Statistique univariée quartile Statistique univariée R R Markdown R Markdown : les rapports automatisés rapport des cotes Régression logistique binaire, multinomiale et ordinale raster Analyse spatiale recodage de variables Recodage de variables recyclage Premier contact recycling rule Premier contact réel, nombre Vecteurs, indexation et assignation référence, modalité Régression logistique binaire, multinomiale et ordinale régression linéaire Statistique bivariée régression logistique Données pondérées Régression logistique binaire, multinomiale et ordinale régression logistique binaire Régression logistique binaire, multinomiale et ordinale régression logistique multinomiale Régression logistique binaire, multinomiale et ordinale NA régression logistique ordinaire Régression logistique binaire, multinomiale et ordinale régression logistique ordinale Régression logistique binaire, multinomiale et ordinale NA régression, droite Graphiques univariés et bivariés avec ggplot2 Statistique bivariée régulière, expression Expressions régulières Manipuler du texte avec stringr relatif, risque Régression logistique binaire, multinomiale et ordinale relation fonctionnelle Formules lattice : graphiques et formules répartition empirique, fonction Graphiques univariés et bivariés avec ggplot2 Statistique univariée répertoire de travail Organiser ses fichiers réplication, poids Régression logistique binaire, multinomiale et ordinale résidus de Schoenfeld Analyse de survie résidus du modèle Modèles à effets aléatoires (modèles mixtes et GEE) résidus, test du Chi² Comparaisons (moyennes et proportions) résolution Export de graphiques ressemblance Classification ascendante hiérarchique (CAH) révolu, âge Calculer un âge risque relatif Comparaisons (moyennes et proportions) Régression logistique binaire, multinomiale et ordinale S Sankey, diagramme Autres extensions graphiques SAS, fichier Import de données saut d’inertie Analyse de séquences Classification ascendante hiérarchique (CAH) Schoenfeld, résidus Analyse de survie scientifique, notation Comparaisons (moyennes et proportions) script Organiser ses fichiers Premier travail avec des données scripts Présentation et Philosophie secteur, diagramme Statistique univariée section Premier travail avec des données sélection descendante pas à pas Régression logistique binaire, multinomiale et ordinale séparateur de champs Import de données séparateur décimal Import de données séquence, analyse Analyse de séquences NA séquence, tapis Analyse de séquences Shapiro-Wilk, test de normalité Comparaisons (moyennes et proportions) similarité, indice Classification ascendante hiérarchique (CAH) sous-échantillon Définir un plan d’échantillonnage complexe Données pondérées SPSS, fichier Import de données statistique bivariée Statistique bivariée statistique descriptive Statistique bivariée Statistique univariée statistique univariée Statistique univariée strate Définir un plan d’échantillonnage complexe stratifié, échantillonnage Définir un plan d’échantillonnage complexe structure d’un objet Premier travail avec des données Visualiser ses données Student, test-t Données pondérées Student, test t Comparaisons (moyennes et proportions) survie, analyse Analyse de survie NA survie, modèle multi-états NA T tableau croisé Données pondérées Statistique bivariée tableau croisé, coefficient de contingence de Cramer Comparaisons (moyennes et proportions) tableau croisé, graphique en mosaïque Statistique bivariée tableau croisé, test exact de Fisher Comparaisons (moyennes et proportions) tableau de donnée Premier travail avec des données tableau de données, fusion Fusion de tables tableau de données, tri Tris tableau de fréquences Statistique univariée tagged missing value Import de données tagged NA Import de données tapis Analyse de séquences task view Extensions (installation, mise à jour) test d’égalité des variances Comparaisons (moyennes et proportions) test de comparaison de deux proportions Comparaisons (moyennes et proportions) test de normalité de Shapiro-Wilk Comparaisons (moyennes et proportions) test de Wilcoxon/Mann-Whitney Comparaisons (moyennes et proportions) test du Chi² Comparaisons (moyennes et proportions) Données pondérées test du Chi², résidus Comparaisons (moyennes et proportions) NA test exact de Fisher Comparaisons (moyennes et proportions) test t de Student Comparaisons (moyennes et proportions) Données pondérées texte Vecteurs, indexation et assignation texte tabulé, fichier Import de données texte, fichier Import de données tibble Import de données tidy data Introduction au tidyverse Réorganiser ses données avec tidyr NA tidyverse Extensions (installation, mise à jour) Import de données Introduction au tidyverse total Données pondérées trajectoire biographique Analyse de séquences transversale, entropie Analyse de séquences tri à plat Données pondérées Statistique univariée tri à plat, ordonner Statistique univariée U univariée, statistique Données pondérées Statistique univariée V valeur booléenne Vecteurs, indexation et assignation valeur logique Vecteurs, indexation et assignation valeur manquante Classification ascendante hiérarchique (CAH) Import de données Statistique bivariée Statistique univariée Vecteurs, indexation et assignation valeur manquante déclarée Import de données valeur, étiquette Facteurs et vecteurs labellisés Import de données variable Premier travail avec des données variable catégorielle Facteurs et vecteurs labellisés variable d’intérêt Régression logistique binaire, multinomiale et ordinale variable explicative Régression logistique binaire, multinomiale et ordinale variable labellisée Import de données variable numérique Premier travail avec des données variable qualitative Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Régression logistique binaire, multinomiale et ordinale Statistique bivariée Statistique univariée variable quantitative Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Régression logistique binaire, multinomiale et ordinale Statistique bivariée Statistique univariée variable, étiquette Facteurs et vecteurs labellisés Import de données variable, recodage Recodage de variables variance Comparaisons (moyennes et proportions) Données pondérées variance inflation factor Multicolinéarité dans la régression variance, analyse de Régression logistique binaire, multinomiale et ordinale variance, pooled Comparaisons (moyennes et proportions) variance, test d’égalité Comparaisons (moyennes et proportions) vecteur Premier contact Vecteurs, indexation et assignation vecteur labellisé Import de données vector Premier contact viewer Premier travail avec des données Visualiser ses données VIF Multicolinéarité dans la régression violin plot Graphiques univariés et bivariés avec ggplot2 violon, graphique en Graphiques univariés et bivariés avec ggplot2 visionneusee Visualiser ses données W Ward, méthode Classification ascendante hiérarchique (CAH) Wilcoxon, test Comparaisons (moyennes et proportions) Index des fonctions % . A B C D E F G H I J K L M N O P Q R S T U V W X Y % %in% (base) Conditions et comparaisons . .N (data.table) Manipulations avancées avec data.table A A2Rplot (JLutils) Classification ascendante hiérarchique (CAH) NA aaply (plyr) Vectorisation abline (graphics) Statistique bivariée add.NA (base) Régression logistique binaire, multinomiale et ordinale add_global_p (gtsummary) Régression logistique binaire, multinomiale et ordinale addNA (base) Facteurs et vecteurs labellisés addNAstr (questionr) Facteurs et vecteurs labellisés Régression logistique binaire, multinomiale et ordinale adply (plyr) Vectorisation aes (ggplot2) Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques NA aggregate (stats) Analyse de séquences Formules agnes (cluster) Classification ascendante hiérarchique (CAH) Classification ascendante hiérarchique (CAH) AIC (stats) Régression logistique binaire, multinomiale et ordinale AIC.svyglm (survey) Régression logistique binaire, multinomiale et ordinale airlines (nycflights13) Manipuler les données avec dplyr airports (nycflights13) Manipuler les données avec dplyr allEffects (effects) Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale Régression logistique binaire, multinomiale et ordinale alply (plyr) Vectorisation anova (geepack) Modèles à effets aléatoires (modèles mixtes et GEE) anova (stats) Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale anti_join (dplyr) Fusion de tables aov (stats) lattice : graphiques et formules append (base) Listes et Tableaux de données apply (base) Vectorisation arrange (dplyr) Manipuler les données avec dplyr Tris array (base) Vectorisation as.character (base) Recodage de variables as.clustrange (WeightedCluster) Classification ascendante hiérarchique (CAH) NA as.data.frame (base) Graphiques univariés et bivariés avec ggplot2 as.data.frame (tibble) Introduction au tidyverse as.data.table (data.table) Manipulations avancées avec data.table as.hclust (cluster) Classification ascendante hiérarchique (CAH) as.integer (base) Définir un plan d’échantillonnage complexe as.matrix (base) Statistique univariée as.numeric (base) Recodage de variables as.seqtree (WeightedCluster) NA as_factor (haven) Recodage de variables as_tibble (tibble) Introduction au tidyverse Manipuler les données avec dplyr B barchart (lattice) lattice : graphiques et formules barplot (graphics) Analyse des correspondances multiples (ACM) base (base) Vectorisation beep (beepr) Structures conditionnelles best.cutree (JLutils) Classification ascendante hiérarchique (CAH) bind_cols (dplyr) Fusion de tables bind_rows (dplyr) Fusion de tables NA biplot (ade4) Analyse des correspondances multiples (ACM) boxplot (ade4) Analyse des correspondances multiples (ACM) boxplot (graphics) lattice : graphiques et formules Statistique bivariée Statistique univariée break (base) Structures conditionnelles brewer.pal (RColorBrewer) Analyse des correspondances multiples (ACM) bwplot (lattice) lattice : graphiques et formules by (base) Sous-ensembles C c (base) Manipulations avancées avec data.table Premier contact Vecteurs, indexation et assignation CA (FactoMineR) Analyse des correspondances multiples (ACM) case_when (dplyr) Manipuler les données avec dplyr Recodage de variables cbind (base) Fusion de tables chisq.residuals (questionr) Comparaisons (moyennes et proportions) chisq.test (stats) Comparaisons (moyennes et proportions) Données pondérées class (base) Facteurs et vecteurs labellisés Vecteurs, indexation et assignation NA clm (ordinal) Régression logistique binaire, multinomiale et ordinale clmm (ordinal) Régression logistique binaire, multinomiale et ordinale NA cm.colors (grDevices) Couleurs et Palettes cmdscale (stats) Analyse de séquences coef (stats) Régression logistique binaire, multinomiale et ordinale coef (survey) Régression logistique binaire, multinomiale et ordinale collect (dplyr) Import de données colorblind_pal (ggthemes) Couleurs et Palettes colors (grDevices) Statistique univariée column_to_rownames (tibble) Introduction au tidyverse comma (scales) Mettre en forme des nombres complete (tidyr) Réorganiser ses données avec tidyr complete.cases (stats) Analyse des correspondances multiples (ACM) Statistique bivariée confint (stats) Régression logistique binaire, multinomiale et ordinale confint (survey) Intervalles de confiance Régression logistique binaire, multinomiale et ordinale confint (svrepmisc) Régression logistique binaire, multinomiale et ordinale contains (dplyr) Manipuler les données avec dplyr Visualiser ses données contour (graphics) Statistique bivariée coord_capped_cart (lemon) Étendre ggplot2 coord_capped_flip (lemon) Étendre ggplot2 coord_flip (ggplot2) Graphiques univariés et bivariés avec ggplot2 copy_labels_from (labelled) Analyse de survie cor (stats) Statistique bivariée corresp (MASS) Analyse des correspondances multiples (ACM) count (dplyr) Manipuler les données avec dplyr cox.zph (survival) Analyse de survie cprop (questionr) Données pondérées Statistique bivariée cramer.v (questionr) Comparaisons (moyennes et proportions) create_report (DataExplorer) Visualiser ses données cut (base) Recodage de variables Régression logistique binaire, multinomiale et ordinale cutree (stats) Classification ascendante hiérarchique (CAH) D daisy (cluster) Classification ascendante hiérarchique (CAH) daply (plyr) Vectorisation data (utils) Premier travail avec des données data.frame (base) Listes et Tableaux de données Manipuler les données avec dplyr datatable (DT) R Markdown : les rapports automatisés dbConnect (DBI) Import de données dbDisconnect (DBI) Import de données dbGetQuery (DBI) Import de données dbListFields (DBI) Import de données dbListTables (DBI) Import de données dbReadTable (DBI) Import de données dcast (data.table) Manipulations avancées avec data.table Réorganiser ses données avec tidyr ddply (plyr) Vectorisation density (stats) Statistique univariée densityplot (lattice) lattice : graphiques et formules describe (questionr) Facteurs et vecteurs labellisés Import de données Listes et Tableaux de données Premier travail avec des données Visualiser ses données NA dev.off (grDevices) Export de graphiques dev.print (grDevices) Export de graphiques dim (base) Formules Listes et Tableaux de données Premier travail avec des données dimdesc (FactoMineR) Analyse des correspondances multiples (ACM) display.brewer.all (RColorBrewer) Analyse des correspondances multiples (ACM) disscenter (TraMineR) Analyse de séquences dist (stats) Classification ascendante hiérarchique (CAH) dist.dudi (ade4) Classification ascendante hiérarchique (CAH) distinct (dplyr) Manipuler les données avec dplyr diverging_bar_chart (ggcharts) Étendre ggplot2 diverging_lollipop_chart (ggcharts) Étendre ggplot2 dlply (plyr) Vectorisation do.call (base) Vectorisation dotchart (graphics) Statistique univariée dotplot (lattice) lattice : graphiques et formules dput (base) Recodage de variables drop1 (stats) Régression logistique binaire, multinomiale et ordinale dudi.acm (ade4) Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) dudi.coa (ade4) Analyse des correspondances multiples (ACM) dudi.mix (ade4) Analyse des correspondances multiples (ACM) dudi.pca (ade4) Analyse des correspondances multiples (ACM) Duration (lubridate) Calculer un âge E effect (effects) Effets d’interaction dans un modèle else (base) Structures conditionnelles ends_width (dplyr) Manipuler les données avec dplyr example (utils) Où trouver de l’aide ? exp (base) Régression logistique binaire, multinomiale et ordinale NA expression (base) Annotations mathématiques extract (tidyr) Manipuler du texte avec stringr Réorganiser ses données avec tidyr F facet_grid (ggplot2) Analyse de séquences Formules Introduction à ggplot2, la grammaire des graphiques lattice : graphiques et formules Régression logistique binaire, multinomiale et ordinale facet_rep_grid (lemon) Étendre ggplot2 facet_rep_wrap (lemon) Étendre ggplot2 facet_wrap (ggplot2) Formules Introduction à ggplot2, la grammaire des graphiques lattice : graphiques et formules factor (base) Facteurs et vecteurs labellisés Recodage de variables FAMD (FactoMineR) Analyse des correspondances multiples (ACM) fct_collapse (forcats) Recodage de variables fct_explicit_na (forcats) Recodage de variables fct_inorder (forcats) NA fct_lump (forcats) Recodage de variables fct_other (forcats) Recodage de variables fct_recode (forcats) Recodage de variables fct_reorder (forcats) Recodage de variables few_pal (ggthemes) Couleurs et Palettes filled.contour (graphics) Statistique bivariée filter (dplyr) Manipuler du texte avec stringr Manipuler les données avec dplyr Sous-ensembles finalfit (finalfit) Régression logistique binaire, multinomiale et ordinale first (data.table) Manipulations avancées avec data.table fisher.test (stats) Comparaisons (moyennes et proportions) fixed (stringr) Manipuler du texte avec stringr flights (nycflights13) Manipuler les données avec dplyr for (base) Structures conditionnelles NA forest_model (forestmodel) Régression logistique binaire, multinomiale et ordinale format (base) Premier contact formula (stats) Formules freq (questionr) Données pondérées Import de données R Markdown : les rapports automatisés Recodage de variables Régression logistique binaire, multinomiale et ordinale Statistique univariée ft_pal (hrbrthemes) Couleurs et Palettes full_join (dplyr) Fusion de tables G gather (tidyr) Analyse de séquences Réorganiser ses données avec tidyr geom_alluvium (ggalluvial) Autres extensions graphiques geom_area (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_bar (ggplot2) Graphiques univariés et bivariés avec ggplot2 NA geom_bin2d (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_boxplot (ggplot2) Graphiques univariés et bivariés avec ggplot2 lattice : graphiques et formules geom_braket (ggpubr) Étendre ggplot2 geom_count (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_density (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_density_2d (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_density_ridges_gradient (ggridges) Étendre ggplot2 geom_dumbbell (ggalt) Étendre ggplot2 geom_errorbarh (ggplot2) Régression logistique binaire, multinomiale et ordinale geom_hex (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_histogram (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_label_repel (ggrepel) Étendre ggplot2 geom_line (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_lollipop (ggalt) Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 geom_mosaic (ggmosaic) Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 geom_pirate (ggpirate) Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 geom_point (ggplot2) Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques geom_raster (ggplot2) Analyse de séquences NA geom_rug (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_smooth (ggplot2) Données pondérées Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques geom_stratum (ggalluvial) Autres extensions graphiques geom_text (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_text_repel (ggrepel) Étendre ggplot2 geom_violin (ggplot2) Graphiques univariés et bivariés avec ggplot2 geom_vline (ggplot2) Introduction à ggplot2, la grammaire des graphiques Régression logistique binaire, multinomiale et ordinale geom_waffle (waffle) Étendre ggplot2 get_legend (cowplot) Combiner plusieurs graphiques getValues (raster) Import de données getwd (base) Organiser ses fichiers ggcoef (GGally) Analyse de survie Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale ggcorr (GGally) Graphiques univariés et bivariés avec ggplot2 ggcoxzph (survminer) Analyse de survie ggcross (JLutils) NA ggdendrogram (ggdendro) Autres extensions graphiques ggeffect (ggeffect) Régression logistique binaire, multinomiale et ordinale ggeffect (ggeffects) Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale ggforest (survminer) Analyse de survie gghiglight (gghiglight) Étendre ggplot2 ggpairs (GGally) Graphiques univariés et bivariés avec ggplot2 ggplot (ggplot2) Données pondérées Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques ggplotly (plotly) Graphiques interactifs ggsave (ggplot2) Export de graphiques Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques ggsurv (GGally) Analyse de survie ggsurvey (JLutils) Données pondérées ggsurvplot (survminer) Analyse de survie ggtitle (ggplot2) Graphiques univariés et bivariés avec ggplot2 glimpse (dplyr) Facteurs et vecteurs labellisés glimpse (tibble) Listes et Tableaux de données Visualiser ses données glm (stats) Données pondérées Régression logistique binaire, multinomiale et ordinale glmulti (ggmulti) Effets d’interaction dans un modèle grid_arrange_shared_legend (lemeon) Combiner plusieurs graphiques group_by (dplyr) Manipuler les données avec dplyr grViz (DiagrammeR) Autres extensions graphiques H haven_labelled (haven) Import de données haven_labelled (labelled) Facteurs et vecteurs labellisés hazard.msm (msm) NA hclust (fastcluster) Classification ascendante hiérarchique (CAH) hclust (stats) Classification ascendante hiérarchique (CAH) HCPC (FactoMineR) Classification ascendante hiérarchique (CAH) hdv2003 (questionr) Formules Premier travail avec des données head (utils) Conditions et comparaisons Listes et Tableaux de données Premier travail avec des données heat.colors (grDevices) Couleurs et Palettes help (utils) Où trouver de l’aide ? Premier contact help.search (utils) Où trouver de l’aide ? help.start (utils) Où trouver de l’aide ? hist (graphics) Graphiques univariés et bivariés avec ggplot2 Statistique univariée histogram (lattice) lattice : graphiques et formules I icut (questionr) Recodage de variables if (base) Structures conditionnelles if_else (dplyr) Manipuler les données avec dplyr Recodage de variables ifelse (base) Recodage de variables image (graphics) Statistique bivariée inertia.dudi (ade4) Analyse des correspondances multiples (ACM) inner_join (dplyr) Fusion de tables install.packages (utils) Extensions (installation, mise à jour) install_github (devtools) Analyse de séquences Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Extensions (installation, mise à jour) Intervalles de confiance interaction (base) Recodage de variables Interval (lubridate) Calculer un âge invisible (base) Vectorisation iorder (questionr) Facteurs et vecteurs labellisés ipsum_pal (hrbrthemes) Couleurs et Palettes irec (questionr) Recodage de variables is.na (base) Recodage de variables Vecteurs, indexation et assignation is.numeric (base) Facteurs et vecteurs labellisés J jpeg (grDevices) Export de graphiques K kable (knitr) R Markdown : les rapports automatisés Régression logistique binaire, multinomiale et ordinale kde2d (MASS) Graphiques univariés et bivariés avec ggplot2 Statistique bivariée L labelled (labelled) Facteurs et vecteurs labellisés labs (ggplot2) Graphiques univariés et bivariés avec ggplot2 lag (dplyr) Manipuler les données avec dplyr lapply (base) Vectorisation Vectorisation last (data.table) Manipulations avancées avec data.table lcmm (lcmm) NA lead (dplyr) Manipuler les données avec dplyr left_join (dplyr) Fusion de tables length (base) Listes et Tableaux de données Premier contact Vecteurs, indexation et assignation letters (base) Vecteurs, indexation et assignation LETTERS (base) Vecteurs, indexation et assignation levels (base) Facteurs et vecteurs labellisés Régression logistique binaire, multinomiale et ordinale Tris library (base) Extensions (installation, mise à jour) Organiser ses fichiers Premier travail avec des données list (base) Listes et Tableaux de données Manipulations avancées avec data.table llpply (plyr) Vectorisation lm (stats) Données pondérées lattice : graphiques et formules Statistique bivariée load (base) Import de données log (base) Vectorisation lookfor (questionr) Facteurs et vecteurs labellisés Listes et Tableaux de données Visualiser ses données lprop (questionr) Données pondérées Statistique bivariée ltabs (questionr) Statistique bivariée M makeCodebook (dataMaid) Visualiser ses données maply (plyr) Vectorisation mapply (base) Vectorisation matches (dplyr) Manipuler les données avec dplyr max (base) Manipulations avancées avec data.table Premier contact Premier travail avec des données Statistique univariée mca (MASS) Analyse des correspondances multiples (ACM) MCA (FactoMineR) Analyse des correspondances multiples (ACM) mcmcsamp (lme4) Modèles à effets aléatoires (modèles mixtes et GEE) mdply (plyr) Vectorisation mean (base) Données pondérées Manipulations avancées avec data.table Où trouver de l’aide ? Premier contact Premier travail avec des données Sous-ensembles Statistique univariée median (stats) Formules Manipulations avancées avec data.table Premier travail avec des données Statistique univariée melt (data.table) Manipulations avancées avec data.table Réorganiser ses données avec tidyr merge (base) Fusion de tables Import de données merge (data.table) Fusion de tables mermaid (DiagrammeR) Autres extensions graphiques min (base) Manipulations avancées avec data.table Premier contact Premier travail avec des données Statistique univariée mlply (plyr) Vectorisation month.abb (base) Vecteurs, indexation et assignation month.name (base) Vecteurs, indexation et assignation mosaic (vcd) Statistique bivariée mosaicplot (graphics) Statistique bivariée msm (msm) NA multinom (nnet) Régression logistique binaire, multinomiale et ordinale multiplot (JLutils) Combiner plusieurs graphiques mutate (dplyr) Import de données Manipuler les données avec dplyr N NA (base) Vecteurs, indexation et assignation na_range (labelled) Import de données na_values (labelled) Import de données names (base) Listes et Tableaux de données Premier travail avec des données Recodage de variables Vecteurs, indexation et assignation Visualiser ses données ncol (base) Listes et Tableaux de données Premier travail avec des données nesting (tidyr) Réorganiser ses données avec tidyr next (base) Structures conditionnelles nolabel_to_na (labelled) Facteurs et vecteurs labellisés nrow (base) Listes et Tableaux de données Premier travail avec des données nth (dplyr) Manipulations avancées avec data.table NULL (base) Vecteurs, indexation et assignation number (scales) Mettre en forme des nombres NA O odds.ratio (questionr) Comparaisons (moyennes et proportions) Régression logistique binaire, multinomiale et ordinale oneway.test (stats) Comparaisons (moyennes et proportions) or_plot (final_fit) Régression logistique binaire, multinomiale et ordinale or_plot (finalfilt) Régression logistique binaire, multinomiale et ordinale order (base) Tris order (data.table) Tris ordgee (geepack) NA ordinal (scales) Mettre en forme des nombres ordLORgee (multgee) NA P paged_table (rmarkdown) R Markdown : les rapports automatisés pairs (graphics) Graphiques univariés et bivariés avec ggplot2 Statistique bivariée panel.xyplot (lattice) lattice : graphiques et formules par (graphics) Analyse des correspondances multiples (ACM) Comparaisons (moyennes et proportions) paste (base) Annotations mathématiques Manipuler du texte avec stringr paste0 (base) Manipuler du texte avec stringr PCA (FactoMineR) Analyse des correspondances multiples (ACM) pdf (grDevices) Export de graphiques percent (scales) Graphiques univariés et bivariés avec ggplot2 Mettre en forme des nombres Period (lubridate) Calculer un âge pi (base) Vecteurs, indexation et assignation pie (graphics) Statistique univariée pirateplot (yarr) Statistique bivariée pivot_longer (tidyr) Réorganiser ses données avec tidyr pivot_wider (tidyr) Réorganiser ses données avec tidyr plot (effects) Régression logistique binaire, multinomiale et ordinale Régression logistique binaire, multinomiale et ordinale plot (FactoMineR) Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) plot (graphics) Introduction à ggplot2, la grammaire des graphiques Premier travail avec des données Statistique bivariée Statistique univariée plot (stats) Analyse de séquences Classification ascendante hiérarchique (CAH) Statistique univariée Statistique univariée plot.prevalence.msm (msm) NA plot_grid (cowplot) Combiner plusieurs graphiques Régression logistique binaire, multinomiale et ordinale plotellipses (FactoMineR) Analyse des correspondances multiples (ACM) plotmath (grDevices) Annotations mathématiques png (grDevices) Export de graphiques position_dodge (ggplot2) Régression logistique binaire, multinomiale et ordinale position_fill (ggplot2) Graphiques univariés et bivariés avec ggplot2 position_stack (ggplot2) Graphiques univariés et bivariés avec ggplot2 postprob (lcmm) NA postscript (grDevices) Export de graphiques predict (stats) Régression logistique binaire, multinomiale et ordinale predict (survey) Régression logistique binaire, multinomiale et ordinale prevalence.msm (msm) NA princomp (stats) Analyse des correspondances multiples (ACM) print (questionr) Statistique bivariée progress_bar (progress) Structures conditionnelles prop (questionr) Statistique bivariée prop.ci (JLutils) Intervalles de confiance prop.ci.lower (JLutils) Intervalles de confiance prop.ci.upper (JLutils) Intervalles de confiance prop.table (base) Statistique univariée prop.test (stats) Comparaisons (moyennes et proportions) Intervalles de confiance ptol_pal (ggthemes) Couleurs et Palettes pvalue (scales) Mettre en forme des nombres NA pyramid_chart (ggcharts) Étendre ggplot2 Q qnorm (stats) Comparaisons (moyennes et proportions) qplot (ggplot2) Introduction à ggplot2, la grammaire des graphiques quant.cut (questionr) Recodage de variables quantile (stats) Statistique univariée R rainbow (grDevices) Couleurs et Palettes range (base) Statistique univariée range (stats) Formules rank (base) Analyse de survie raster (raster) Import de données rbind (base) Fusion de tables rbind.fill (plyr) Fusion de tables read.csv (utils) Import de données Introduction à ggplot2, la grammaire des graphiques read.csv2 (utils) Import de données read.dbf (foreign) Import de données read.delim (utils) Import de données read.delim2 (utils) Import de données read.dta (foreign) Import de données read.spss (foreign) Import de données read.table (utils) Import de données read.xlsx (openxlsx) Import de données read.xlsx (xlsx) Import de données read.xlsx2 (xlsx) Import de données read.xport (foreign) Import de données read_csv (readr) Import de données Introduction à ggplot2, la grammaire des graphiques read_csv2 (readr) Import de données read_delim (readr) Import de données read_dta (haven) Import de données read_excel (readxl) Import de données read_feather (feather) Export de données read_sas (haven) Import de données read_spss (haven) Import de données read_stata (haven) Import de données read_tsv (readr) Import de données readAsciiGrid (maptools) Import de données readShapeLines (maptools) Import de données readShapePoints (maptools) Import de données readShapePoly (maptools) Import de données readShapeSpatial (maptools) Import de données recode (dplyr) Manipuler les données avec dplyr recode_factor (dplyr) Manipuler les données avec dplyr rect.hclust (stats) Classification ascendante hiérarchique (CAH) regex (stringr) Manipuler du texte avec stringr registerDoMC (doMC) Vectorisation relevel (stats) Analyse de survie Intervalles de confiance Régression logistique binaire, multinomiale et ordinale relrisk (mosaic) Comparaisons (moyennes et proportions) remove.packages (utils) Extensions (installation, mise à jour) remove_labels (labelled) Recodage de variables rename (dplyr) Fusion de tables Manipuler les données avec dplyr rename.variable (questionr) Recodage de variables rep (base) Vecteurs, indexation et assignation repeat (base) Structures conditionnelles require (base) Extensions (installation, mise à jour) rgb (grDevices) Couleurs et Palettes right_join (dplyr) Fusion de tables row.names (base) Listes et Tableaux de données rownames_to_column (tibble) Introduction au tidyverse rownames_to_columns (tibble) Introduction au tidyverse rprop (questionr) Statistique bivariée rug (graphics) lattice : graphiques et formules Statistique univariée runif (stats) Analyse de survie S s.arrow (ade4) Analyse des correspondances multiples (ACM) s.chull (ade4) Analyse des correspondances multiples (ACM) s.class (ade4) Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) s.corcicle (ade4) Analyse des correspondances multiples (ACM) s.freq (JLutils) Analyse des correspondances multiples (ACM) s.hist (ade4) Analyse des correspondances multiples (ACM) s.label (ade4) Analyse des correspondances multiples (ACM) s.value (ade4) Analyse des correspondances multiples (ACM) sample_frac (dplyr) Manipuler les données avec dplyr sample_n (dplyr) Manipuler les données avec dplyr sankeyNetwork (networkD3) Autres extensions graphiques sapply (base) Vectorisation save (base) Import de données save.image (base) Import de données scale_color_paletteer_c (paletteer) Couleurs et Palettes scale_color_paletteer_d (paletteer) Couleurs et Palettes scale_colour_brewer (ggplot2) Couleurs et Palettes Introduction à ggplot2, la grammaire des graphiques scale_colour_viridis_c (ggplot2) Couleurs et Palettes scale_colour_viridis_d (ggplot2) Couleurs et Palettes scale_coulour_gradientn (ggplot2) Couleurs et Palettes scale_fill_brewer (ggplot2) Couleurs et Palettes scale_fill_gradientn (ggplot2) Couleurs et Palettes scale_fill_viridis_c (ggplot2) Couleurs et Palettes scale_fill_viridis_d (ggplot2) Couleurs et Palettes scale_x_continuous (ggplot2) Introduction à ggplot2, la grammaire des graphiques scale_x_log10 (ggplot2) Régression logistique binaire, multinomiale et ordinale scale_y_continuous (ggplot2) Graphiques univariés et bivariés avec ggplot2 scatter (ade4) Analyse des correspondances multiples (ACM) score (ade4) Analyse des correspondances multiples (ACM) screeplot (ade4) Analyse des correspondances multiples (ACM) sd (stats) Premier contact Statistique univariée select (dplyr) Manipuler les données avec dplyr Réorganiser ses données avec tidyr select_helpers (tidyselect) Réorganiser ses données avec tidyr semi_join (dplyr) Fusion de tables separate (tidyr) Manipuler du texte avec stringr Réorganiser ses données avec tidyr seq (base) Vecteurs, indexation et assignation seq_heatmap (JLutils) Analyse de séquences NA seqcost (TraMineR) NA seqdef (TraMineR) Analyse de séquences NA seqdist (TraMineR) Analyse de séquences Classification ascendante hiérarchique (CAH) seqdplot (TraMineR) Analyse de séquences NA seqfplot (TraMineR) Analyse de séquences seqHtplot (TraMineR) Analyse de séquences seqIplot (TraMineR) Analyse de séquences NA seqlength (TraMineR) NA seqmsplot (TraMineR) Analyse de séquences seqmtplot (TraMineR) Analyse de séquences seqplot (TraMineR) NA seqrep (TraMineR) Analyse de séquences seqrplot (TraMineR) Analyse de séquences seqsubm (TraMineR) Analyse de séquences seqtreedisplay (TraMineR) NA setDF (data.table) Manipulations avancées avec data.table setDT (data.table) Analyse de survie Manipulations avancées avec data.table setorder (data.table) Tris setwd (base) Organiser ses fichiers shapiro.test (stats) Comparaisons (moyennes et proportions) skim (skimr) Visualiser ses données slice (dplyr) Manipuler les données avec dplyr smean.sd (Hmisc) Formules smoothScatter (graphics) Statistique bivariée sort (base) Statistique univariée Tris sort_val_labels (labelled) Facteurs et vecteurs labellisés source (base) Organiser ses fichiers SpatialGridDataFrame (sp) Import de données spread (tidyr) Réorganiser ses données avec tidyr NA starts_with (dplyr) Manipuler les données avec dplyr stat_count (ggplot2) Graphiques univariés et bivariés avec ggplot2 Graphiques univariés et bivariés avec ggplot2 stat_density_2d (ggplot2) Graphiques univariés et bivariés avec ggplot2 stat_ecdf (ggplot2) Graphiques univariés et bivariés avec ggplot2 stat_prop (JLutils) Graphiques univariés et bivariés avec ggplot2 NA statetable.msm (msm) NA step (stats) Analyse de survie Régression logistique binaire, multinomiale et ordinale stepAIC (MASS) Régression logistique binaire, multinomiale et ordinale str (utils) Formules Listes et Tableaux de données Premier travail avec des données Statistique univariée Visualiser ses données str_c (stringr) Manipuler du texte avec stringr str_count (stringr) Manipuler du texte avec stringr str_detect (stringr) Manipuler du texte avec stringr NA str_extract (stringr) Manipuler du texte avec stringr str_extract_all (stringr) Manipuler du texte avec stringr str_glue_data (stringr) Manipuler du texte avec stringr str_replace (stringr) Manipuler du texte avec stringr str_replace_all (stringr) Manipuler du texte avec stringr str_split (stringr) Manipuler du texte avec stringr str_sub (stringr) Analyse de séquences Manipuler du texte avec stringr str_subset (stringr) Manipuler du texte avec stringr str_to_lower (stringr) Manipuler du texte avec stringr str_to_title (stringr) Manipuler du texte avec stringr str_to_upper (stringr) Manipuler du texte avec stringr subset (base) Sous-ensembles subset (data.table) Sous-ensembles subset (survey) Définir un plan d’échantillonnage complexe Données pondérées substitute (base) Annotations mathématiques sum (base) Vectorisation summarise (dplyr) Formules Manipuler les données avec dplyr Réorganiser ses données avec tidyr summarize (Hmisc) Formules summary (ade4) Analyse des correspondances multiples (ACM) summary (base) Conditions et comparaisons lattice : graphiques et formules Listes et Tableaux de données Premier travail avec des données Recodage de variables Statistique univariée Visualiser ses données NA summary (stats) Régression logistique binaire, multinomiale et ordinale summary_factorlist (finalfit) Régression logistique binaire, multinomiale et ordinale NA Surv (survival) Analyse de survie survdiff (survival) Analyse de survie survfit (survival) Analyse de survie svg (grDevices) Export de graphiques svyboxplot (survey) Données pondérées svyby (survey) Données pondérées svychisq (survey) Comparaisons (moyennes et proportions) Données pondérées svyciprop (survey) Données pondérées Intervalles de confiance svyclm (svrepmisc) Régression logistique binaire, multinomiale et ordinale svycoxph (survey) Analyse de survie svydesign (survey) Définir un plan d’échantillonnage complexe Données pondérées svyglm (survey) Données pondérées Régression logistique binaire, multinomiale et ordinale svyhist (survey) Données pondérées svykm (survey) Analyse de survie svymean (survey) Données pondérées svymultinom (svrepmisc) Régression logistique binaire, multinomiale et ordinale svyolr (survey) Régression logistique binaire, multinomiale et ordinale svyplot (survey) Données pondérées svyquantile (survey) Données pondérées svyranktest (survey) Comparaisons (moyennes et proportions) svyratio (survey) Données pondérées svytable (survey) Comparaisons (moyennes et proportions) Données pondérées svytotal (survey) Données pondérées svyttest (survey) Comparaisons (moyennes et proportions) Données pondérées svyvar (survey) Données pondérées switch (base) Structures conditionnelles T t.test (stats) Comparaisons (moyennes et proportions) Intervalles de confiance table (base) Comparaisons (moyennes et proportions) Conditions et comparaisons Données pondérées Facteurs et vecteurs labellisés Formules Intervalles de confiance Premier travail avec des données R Markdown : les rapports automatisés Recodage de variables Statistique bivariée Statistique univariée tagged_na (haven) Import de données tail (utils) Listes et Tableaux de données Premier travail avec des données tally (dplyr) Manipuler les données avec dplyr tapply (base) Comparaisons (moyennes et proportions) Données pondérées Sous-ensembles Statistique bivariée Vectorisation tbl (dplyr) Import de données tbl_df (dplyr) Analyse de survie Import de données tbl_merge (gtsummary) Régression logistique binaire, multinomiale et ordinale tbl_regression (gtsummary) Régression logistique binaire, multinomiale et ordinale tbl_summary (gtsummary) Régression logistique binaire, multinomiale et ordinale terrain.colors (grDevices) Couleurs et Palettes theme (ggplot2) Graphiques univariés et bivariés avec ggplot2 theme_tufte (ggthemes) Étendre ggplot2 tibble (tibble) Introduction au tidyverse Manipuler les données avec dplyr tidy (broom) Analyse de survie Graphiques univariés et bivariés avec ggplot2 Graphiques univariés et bivariés avec ggplot2 Régression logistique binaire, multinomiale et ordinale Régression logistique 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data.table Manipuler du texte avec stringr Manipuler les données avec dplyr Organiser ses fichiers Où trouver de l’aide ? Premier contact Premier travail avec des données R Markdown : les rapports automatisés Recodage de variables Régression logistique binaire, multinomiale et ordinale Sous-ensembles Statistique bivariée Statistique univariée Structures conditionnelles Tris Vecteurs, indexation et assignation Vectorisation Visualiser ses données NA beepr Structures conditionnelles breakDown Effets d’interaction dans un modèle broom Analyse de survie Graphiques univariés et bivariés avec ggplot2 Modèles à effets aléatoires (modèles mixtes et GEE) Régression logistique binaire, multinomiale et ordinale C car Multicolinéarité dans la régression circlize Autres extensions graphiques cluster Analyse de séquences Classification ascendante hiérarchique (CAH) codebook Visualiser ses données cowplot Combiner plusieurs graphiques Régression logistique binaire, multinomiale et ordinale D data.table Analyse de survie Fusion de tables Manipulations avancées avec data.table Manipuler les données avec dplyr Recodage de 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données Manipulations avancées avec data.table Manipuler du texte avec stringr Manipuler les données avec dplyr Recodage de variables Réorganiser ses données avec tidyr Scraping Sous-ensembles Tris Vectorisation Visualiser ses données NA DT R Markdown : les rapports automatisés dtplyr Manipulations avancées avec data.table Manipuler les données avec dplyr E ecdf Statistique univariée effects Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale esquisse ggplot2 et la grammaire des graphiques explor Analyse des correspondances multiples (ACM) extrafont Étendre ggplot2 F factoextra Analyse des correspondances multiples (ACM) FactoInvestigate Analyse des correspondances multiples (ACM) FactoMineR Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Données pondérées Factoshiny Analyse des correspondances multiples (ACM) fastcluster Classification ascendante hiérarchique (CAH) feather Export de données final_fit Régression logistique binaire, multinomiale et ordinale finalfilt Régression logistique binaire, multinomiale et ordinale finalfit Régression logistique binaire, multinomiale et ordinale NA flextable R Markdown : les rapports automatisés forcats Extensions (installation, mise à jour) Introduction au tidyverse Manipuler du texte avec stringr Manipuler les données avec dplyr Recodage de variables NA foreign Export de données Import de données forestmodel Régression logistique binaire, multinomiale et ordinale formattable R Markdown : les rapports automatisés G gapminder Étendre ggplot2 Réorganiser ses données avec tidyr gee Modèles à effets aléatoires (modèles mixtes et GEE) geepack Modèles à effets aléatoires (modèles mixtes et GEE) NA ggalluvial Autres extensions graphiques GGally Analyse de survie Autres extensions graphiques Effets d’interaction dans un modèle Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques Régression logistique binaire, multinomiale et ordinale ggalt Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 gganimate Étendre ggplot2 ggcharts Étendre ggplot2 ggdendro Autres extensions graphiques Classification ascendante hiérarchique (CAH) ggeffect Régression logistique binaire, multinomiale et ordinale ggeffects Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale NA ggendro Autres extensions graphiques ggfortify Introduction à ggplot2, la grammaire des graphiques gghighlight Étendre ggplot2 gghiglight Étendre ggplot2 ggmap Cartes Introduction à ggplot2, la grammaire des graphiques ggmosaic Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 ggmulti Effets d’interaction dans un modèle ggpirate Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 ggplot2 Analyse de séquences Analyse de survie Analyse des correspondances multiples (ACM) Annotations mathématiques Autres extensions graphiques Cartes Classification ascendante hiérarchique (CAH) Combiner plusieurs graphiques Couleurs et Palettes Données pondérées Effets d’interaction dans un modèle Étendre ggplot2 Export de graphiques Extensions (installation, mise à jour) Formules ggplot2 et la grammaire des graphiques Graphiques interactifs Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques Introduction au tidyverse lattice : graphiques et formules R Markdown : les rapports automatisés Recodage de variables Régression logistique binaire, multinomiale et ordinale Réorganiser ses données avec tidyr NA ggplotAssist ggplot2 et la grammaire des graphiques ggpubr Autres extensions graphiques Étendre ggplot2 ggrepel Étendre ggplot2 ggridges Étendre ggplot2 ggsci Couleurs et Palettes ggThemeAssist ggplot2 et la grammaire des graphiques ggthemes Couleurs et Palettes Étendre ggplot2 Introduction à ggplot2, la grammaire des graphiques ggvis Graphiques interactifs Introduction à ggplot2, la grammaire des graphiques gifski Étendre ggplot2 glmulti Effets d’interaction dans un modèle glue Manipuler du texte avec stringr googlesheets Import de données gpplot2 Couleurs et Palettes graphics Analyse des correspondances multiples (ACM) Comparaisons (moyennes et proportions) Graphiques univariés et bivariés avec ggplot2 Introduction à ggplot2, la grammaire des graphiques lattice : graphiques et formules Premier travail avec des données Statistique bivariée Statistique univariée grDevices Annotations mathématiques Couleurs et Palettes Export de graphiques Statistique univariée grid lattice : graphiques et formules gridExtra Modèles à effets aléatoires (modèles mixtes et GEE) gt Couleurs et Palettes R Markdown : les rapports automatisés gtsummary Effets d’interaction dans un modèle Régression logistique binaire, multinomiale et ordinale H haven Analyse de survie Export de données Extensions (installation, mise à jour) Facteurs et vecteurs labellisés Import de données Recodage de variables highcharter Autres extensions graphiques Hmisc Facteurs et vecteurs labellisés Formules hrbrthemes Couleurs et Palettes Étendre ggplot2 J JLutils Analyse de séquences Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Combiner plusieurs graphiques Données pondérées Graphiques univariés et bivariés avec ggplot2 Intervalles de confiance Régression logistique binaire, multinomiale et ordinale NA K kableExtra R Markdown : les rapports automatisés Vectorisation khroma Couleurs et Palettes knitr Fusion de tables R Markdown : les rapports automatisés Régression logistique binaire, multinomiale et ordinale L labelled Analyse de survie Effets d’interaction dans un modèle Facteurs et vecteurs labellisés Graphiques univariés et bivariés avec ggplot2 Import de données Recodage de variables Régression logistique binaire, multinomiale et ordinale Visualiser ses données NA lattice Formules Introduction à ggplot2, la grammaire des graphiques lattice : graphiques et formules Modèles à effets aléatoires (modèles mixtes et GEE) latticeExtra lattice : graphiques et formules Modèles à effets aléatoires (modèles mixtes et GEE) lcmm NA lemeon Combiner plusieurs graphiques lemon Combiner plusieurs graphiques Étendre ggplot2 LexisPlotR Diagramme de Lexis lme4 Formules Modèles à effets aléatoires (modèles mixtes et GEE) lubridate Analyse de survie Calculer un âge Extensions (installation, mise à jour) Gestion des dates Import de données Scraping M magrittr Manipuler les données avec dplyr maptools Export de données Import de données mapview Cartes MASS Analyse des correspondances multiples (ACM) Graphiques univariés et bivariés avec ggplot2 Régression logistique binaire, multinomiale et ordinale Statistique bivariée mctest Multicolinéarité dans la régression memisc Facteurs et vecteurs labellisés mlogit Régression logistique binaire, multinomiale et ordinale mosaic Comparaisons (moyennes et proportions) msm NA msSurv NA multgee NA N networkD3 Autres extensions graphiques 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(CAH) Comparaisons (moyennes et proportions) Conditions et comparaisons Données pondérées Effets d’interaction dans un modèle Facteurs et vecteurs labellisés Formules Fusion de tables Graphiques univariés et bivariés avec ggplot2 Import de données Intervalles de confiance Listes et Tableaux de données Multicolinéarité dans la régression Premier travail avec des données R Markdown : les rapports automatisés Recodage de variables Régression logistique binaire, multinomiale et ordinale Réorganiser ses données avec tidyr Sous-ensembles Statistique bivariée Statistique univariée Tris Visualiser ses données NA R raster Analyse spatiale Import de données RColorBrewer Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Couleurs et Palettes Introduction à ggplot2, la grammaire des graphiques reader Export de données readr Export de données Extensions (installation, mise à jour) Formules Import de données Introduction à ggplot2, la grammaire des graphiques Introduction au tidyverse Scraping readxl Extensions (installation, mise à jour) Import de données Introduction au tidyverse reshape2 Extensions (installation, mise à jour) revealjs R Markdown : les rapports automatisés riverplot Autres extensions graphiques rmarkdown R Markdown : les rapports automatisés rmdformats R Markdown : les rapports automatisés rmdshower R Markdown : les rapports automatisés RSQLite Import de données rvest Scraping S sankeyNetwork Autres extensions graphiques scales Étendre ggplot2 Graphiques univariés et bivariés avec ggplot2 Mettre en forme des nombres NA sf Analyse spatiale Cartes sjPlot R Markdown : les rapports automatisés skimr Visualiser ses données sp Analyse spatiale Cartes Import de données srvyr Définir un plan d’échantillonnage complexe Données pondérées Manipuler les données avec dplyr stats Analyse de séquences Analyse de survie Analyse des correspondances multiples (ACM) Classification ascendante hiérarchique (CAH) Comparaisons (moyennes et proportions) Données pondérées Effets d’interaction dans un modèle Formules Graphiques univariés et bivariés avec ggplot2 Intervalles de confiance lattice : graphiques et formules Manipulations avancées avec data.table Premier contact Premier travail avec des données Recodage de variables Régression logistique binaire, multinomiale et ordinale Statistique bivariée Statistique univariée stringi Manipuler du texte avec stringr stringr Analyse de séquences Expressions régulières Extensions (installation, mise à jour) Introduction au tidyverse Manipuler du texte avec stringr Scraping NA survey Analyse de survie Comparaisons (moyennes et proportions) Définir un plan d’échantillonnage complexe Données pondérées Intervalles de confiance Manipuler les données avec dplyr Régression logistique binaire, multinomiale et ordinale survival Analyse de survie NA survminer Analyse de survie svrepmisc Régression logistique binaire, multinomiale et ordinale T tables R Markdown : les rapports automatisés tibble Extensions (installation, mise à jour) Introduction au tidyverse Listes et Tableaux de données Manipulations avancées avec data.table Manipuler les données avec dplyr Visualiser ses données tidyr Analyse de séquences Extensions (installation, mise à jour) Introduction au tidyverse Manipuler du texte avec stringr Réorganiser ses données avec tidyr NA tidyselect Réorganiser ses données avec tidyr tidyverse Autres extensions graphiques Extensions (installation, mise à jour) Fusion de tables Introduction au tidyverse Manipuler du texte avec stringr Manipuler les données avec dplyr Recodage de variables Réorganiser ses données avec tidyr NA tmap Cartes TraMineR Analyse de séquences Classification ascendante hiérarchique (CAH) NA TraMineRextras Analyse de séquences U utils Conditions et comparaisons Export de données Extensions (installation, mise à jour) Formules Import de données Installation de R et RStudio Introduction à ggplot2, la grammaire des graphiques Listes et Tableaux de données Où trouver de l’aide ? Premier contact Premier travail avec des données Statistique univariée Structures conditionnelles Visualiser ses données V vcd Statistique bivariée VGAM Régression logistique binaire, multinomiale et ordinale viridis Autres extensions graphiques Couleurs et Palettes NA W waffle Étendre ggplot2 WeightedCluster Analyse de séquences Classification ascendante hiérarchique (CAH) NA X xlsx Export de données Import de données xtable R Markdown : les rapports automatisés Y yarr Statistique bivariée yarrr Statistique bivariée $(document).ready(function () {
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nom	voiture
Sylvie	Twingo
Sylvie	Ferrari
Monique	Scenic
Gunter	Lada
Rayan	Twingo
Rayan	Clio

nom	voiture	vitesse
Sylvie	Twingo	140
Sylvie	Ferrari	280
Monique	Scenic	NA
Gunter	Lada	85
Rayan	Twingo	140
Rayan	Clio	160

voiture	vitesse	nom
Twingo	140	Sylvie
Twingo	140	Rayan
Ferrari	280	Sylvie
Clio	160	Rayan
Lada	85	Gunter
208	160	NA

nom	voiture	vitesse
Sylvie	Twingo	140
Rayan	Twingo	140
Sylvie	Ferrari	280
Rayan	Clio	160
Gunter	Lada	85
NA	208	160

nom	voiture	vitesse
Sylvie	Twingo	140
Sylvie	Ferrari	280
Gunter	Lada	85
Rayan	Twingo	140
Rayan	Clio	160

nom	adresse	ville
Mr Félicien Machin	3 rue des Fleurs	Nouméa
Mme Raymonde Bidule	47 ave de la Libération	Marseille
M. Martial Truc	12 rue du 17 octobre 1961	Vénissieux
Mme Huguette Chose	221 avenue de la Libération	Marseille

country	1992	1997	2002	2007
Belgium	10045622	10199787	10311970	10392226
France	57374179	58623428	59925035	61083916
Germany	80597764	82011073	82350671	82400996

country	annee	population
Belgium	1992	10045622
France	1992	57374179
Germany	1992	80597764
Belgium	1997	10199787
France	1997	58623428
Germany	1997	82011073
Belgium	2002	10311970
France	2002	59925035
Germany	2002	82350671
Belgium	2007	10392226
France	2007	61083916
Germany	2007	82400996

year	month	day	dep_time	carrier	name	flights_per_year
2013	1	1	517	UA	United Air Lines Inc.	58665
2013	1	1	533	UA	United Air Lines Inc.	58665
2013	1	1	542	AA	American Airlines Inc.	32729
2013	1	1	554	UA	United Air Lines Inc.	58665
2013	1	1	558	AA	American Airlines Inc.	32729
2013	1	1	558	UA	United Air Lines Inc.	58665
2013	1	1	558	UA	United Air Lines Inc.	58665
2013	1	1	559	AA	American Airlines Inc.	32729

country	continent	year	variable	value
Belgium	Europe	2002	lifeExp	78.320
Belgium	Europe	2007	lifeExp	79.441
France	Europe	2002	lifeExp	79.590
France	Europe	2007	lifeExp	80.657
Germany	Europe	2002	lifeExp	78.670
Germany	Europe	2007	lifeExp	79.406
Belgium	Europe	2002	pop	10311970.000
Belgium	Europe	2007	pop	10392226.000
France	Europe	2002	pop	59925035.000
France	Europe	2007	pop	61083916.000
Germany	Europe	2002	pop	82350671.000
Germany	Europe	2007	pop	82400996.000

code_departement	code_commune	commune	pop_tot
01	004	Ambérieu-en-Bugey	14233
01	007	Ambronay	2437
01	014	Arbent	3440
01	024	Attignat	3110
01	025	Bâgé-la-Ville	3130
01	027	Balan	2785

eleve	matiere	note
Alain	Maths	16
Alain	Français	9
Barnabé	Maths	17
Chantal	Français	11

id	eleve	matiere	note
1001001	Alain	Maths	16
1001001	Alain	Français	9
1001002	Barnabé	Maths	17
1001003	Chantal	Français	11

Type de fichier souhaité	Fonction	Extension
texte	`write.table`	`utils`
CSV	`write.csv`	`utils`
CSV	`write_csv`	`readr`
Excel	`write.xlsx`	`xlsx`
dBase	`write.dbf`	`foreign`
SPSS	`write_sav`	`haven`
SPSS	`write.foreign`	`foreign`
Stata	`write.dta`	`foreign`
Stata	`write_dta`	`haven`
SAS	`write.foreign`	`foreign`
SPSS	`write.foreign`	`foreign`

term	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	0.4501	0.3239	-2.4648	0.0137	0.2377	0.8473
sexeHomme	1.5522	0.1061	4.1456	0.0000	1.2614	1.9120
grpage[25,45)	0.6568	0.2281	-1.8436	0.0652	0.4194	1.0275
grpage[45,65)	0.3378	0.2377	-4.5661	0.0000	0.2115	0.5381
grpage[65,99]	0.2512	0.2738	-5.0452	0.0000	0.1464	0.4288
etudSecondaire	2.5872	0.1974	4.8144	0.0000	1.7653	3.8328
etudTechnique/Professionnel	2.8555	0.1898	5.5281	0.0000	1.9805	4.1729
etudSupérieur	6.6304	0.1952	9.6900	0.0000	4.5518	9.7951
etudmanquant	8.5885	0.3302	6.5119	0.0000	4.5348	16.5885
religPratiquant occasionnel	0.9783	0.1892	-0.1158	0.9078	0.6759	1.4199
religAppartenance sans pratique	0.9933	0.1747	-0.0383	0.9694	0.7063	1.4020
religNi croyance ni appartenance	0.8062	0.1931	-1.1155	0.2646	0.5524	1.1782
religRejet	0.6814	0.2859	-1.3415	0.1798	0.3869	1.1890
religNSP ou NVPR	0.9196	0.4110	-0.2039	0.8385	0.3997	2.0216
heures.tv	0.8861	0.0336	-3.5995	0.0003	0.8290	0.9458

Characteristic	OR¹	95% CI¹	p-value
sexe
Femme	—	—
Homme	1.55	1.26, 1.91	<0.001
grpage
[16,25)	—	—
[25,45)	0.66	0.42, 1.03	0.065
[45,65)	0.34	0.21, 0.54	<0.001
[65,99]	0.25	0.15, 0.43	<0.001
etud
Primaire	—	—
Secondaire	2.59	1.77, 3.83	<0.001
Technique/Professionnel	2.86	1.98, 4.17	<0.001
Supérieur	6.63	4.55, 9.80	<0.001
manquant	8.59	4.53, 16.6	<0.001
relig
Pratiquant regulier	—	—
Pratiquant occasionnel	0.98	0.68, 1.42	>0.9
Appartenance sans pratique	0.99	0.71, 1.40	>0.9
Ni croyance ni appartenance	0.81	0.55, 1.18	0.3
Rejet	0.68	0.39, 1.19	0.2
NSP ou NVPR	0.92	0.40, 2.02	0.8
heures.tv	0.89	0.83, 0.95	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

Dependent: Pratique du sport ?		Non	Oui	p
Sexe	Femme	747 (58.5)	354 (49.0)	<0.001
	Homme	530 (41.5)	369 (51.0)
Groupe d’âges	[16,25)	58 (4.5)	111 (15.4)	<0.001
	[25,45)	359 (28.1)	347 (48.0)
	[45,65)	541 (42.4)	204 (28.2)
	[65,99]	319 (25.0)	61 (8.4)
Niveau d’étude	Primaire	416 (32.6)	50 (6.9)	<0.001
	Secondaire	270 (21.1)	117 (16.2)
	Technique/Professionnel	378 (29.6)	216 (29.9)
	Supérieur	186 (14.6)	255 (35.3)
	manquant	27 (2.1)	85 (11.8)
Pratique religieuse	Pratiquant regulier	182 (14.3)	84 (11.6)	0.144
	Pratiquant occasionnel	295 (23.1)	147 (20.3)
	Appartenance sans pratique	473 (37.0)	287 (39.7)
	Ni croyance ni appartenance	239 (18.7)	160 (22.1)
	Rejet	60 (4.7)	33 (4.6)
	NSP ou NVPR	28 (2.2)	12 (1.7)
Nombre d’heures passées devant la télévision par jour	Mean (SD)	2.5 (1.9)	1.8 (1.4)	<0.001

Dependent: Pratique du sport ?		Non	Oui	OR (univariable)	OR (multivariable)
Sexe	Femme	747 (67.8)	354 (32.2)	-	-
	Homme	530 (59.0)	369 (41.0)	1.47 (1.22-1.77, p<0.001)	1.55 (1.26-1.91, p<0.001)
Groupe d’âges	[16,25)	58 (34.3)	111 (65.7)	-	-
	[25,45)	359 (50.8)	347 (49.2)	0.51 (0.35-0.71, p<0.001)	0.66 (0.42-1.03, p=0.065)
	[45,65)	541 (72.6)	204 (27.4)	0.20 (0.14-0.28, p<0.001)	0.34 (0.21-0.54, p<0.001)
	[65,99]	319 (83.9)	61 (16.1)	-	-
Niveau d’étude	Primaire	416 (89.3)	50 (10.7)	-	-
	Secondaire	270 (69.8)	117 (30.2)	3.61 (2.52-5.23, p<0.001)	2.59 (1.77-3.83, p<0.001)
	Technique/Professionnel	378 (63.6)	216 (36.4)	4.75 (3.42-6.72, p<0.001)	2.86 (1.98-4.17, p<0.001)
	Supérieur	186 (42.2)	255 (57.8)	11.41 (8.11-16.31, p<0.001)	6.63 (4.55-9.80, p<0.001)
	manquant	27 (24.1)	85 (75.9)	26.19 (15.74-44.90, p<0.001)	8.59 (4.53-16.59, p<0.001)
Pratique religieuse	Pratiquant regulier	182 (68.4)	84 (31.6)	-	-
	Pratiquant occasionnel	295 (66.7)	147 (33.3)	1.08 (0.78-1.50, p=0.644)	0.98 (0.68-1.42, p=0.908)
	Appartenance sans pratique	473 (62.2)	287 (37.8)	1.31 (0.98-1.78, p=0.071)	0.99 (0.71-1.40, p=0.969)
	Ni croyance ni appartenance	239 (59.9)	160 (40.1)	1.45 (1.05-2.02, p=0.026)	0.81 (0.55-1.18, p=0.265)
	Rejet	60 (64.5)	33 (35.5)	1.19 (0.72-1.95, p=0.489)	0.68 (0.39-1.19, p=0.180)
	NSP ou NVPR	28 (70.0)	12 (30.0)	0.93 (0.44-1.88, p=0.841)	0.92 (0.40-2.02, p=0.838)
Nombre d’heures passées devant la télévision par jour	Mean (SD)	2.5 (1.9)	1.8 (1.4)	0.79 (0.74-0.84, p<0.001)	0.89 (0.83-0.95, p<0.001)
NA	NA	NA	NA	0.10 (0.07-0.15, p<0.001)	0.25 (0.15-0.43, p<0.001)

Characteristic	Modèle complet			Modèle réduit
Characteristic	log(OR)¹	95% CI¹	p-value	log(OR)¹	95% CI¹	p-value
Sexe
Femme	—	—		—	—
Homme	0.44	0.23, 0.65	<0.001	0.42	0.21, 0.62	<0.001
Groupe d'âges
[16,25)	—	—		—	—
[25,45)	-0.42	-0.87, 0.03	0.065	-0.39	-0.84, 0.05	0.084
[45,65)	-1.1	-1.6, -0.62	<0.001	-1.0	-1.5, -0.57	<0.001
[65,99]	-1.4	-1.9, -0.85	<0.001	-1.3	-1.8, -0.78	<0.001
Niveau d'étude
Primaire	—	—		—	—
Secondaire	0.95	0.57, 1.3	<0.001	0.93	0.55, 1.3	<0.001
Technique/Professionnel	1.0	0.68, 1.4	<0.001	1.0	0.67, 1.4	<0.001
Supérieur	1.9	1.5, 2.3	<0.001	1.9	1.5, 2.3	<0.001
manquant	2.2	1.5, 2.8	<0.001	2.1	1.5, 2.8	<0.001
Pratique religieuse
Pratiquant regulier	—	—
Pratiquant occasionnel	-0.02	-0.39, 0.35	>0.9
Appartenance sans pratique	-0.01	-0.35, 0.34	>0.9
Ni croyance ni appartenance	-0.22	-0.59, 0.16	0.3
Rejet	-0.38	-0.95, 0.17	0.2
NSP ou NVPR	-0.08	-0.92, 0.70	0.8
Nombre d'heures passées devant la télévision par jour	-0.12	-0.19, -0.06	<0.001	-0.12	-0.19, -0.06	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

Characteristic	N = 2000¹
Pratique du sport ?
Non	1277 (64%)
Oui	723 (36%)
Sexe
Femme	1101 (55%)
Homme	899 (45%)
Groupe d'âges
[16,25)	169 (8.5%)
[25,45)	706 (35%)
[45,65)	745 (37%)
[65,99]	380 (19%)
Niveau d'étude
Primaire	466 (23%)
Secondaire	387 (19%)
Technique/Professionnel	594 (30%)
Supérieur	441 (22%)
manquant	112 (5.6%)
Pratique religieuse
Pratiquant regulier	266 (13%)
Pratiquant occasionnel	442 (22%)
Appartenance sans pratique	760 (38%)
Ni croyance ni appartenance	399 (20%)
Rejet	93 (4.6%)
NSP ou NVPR	40 (2.0%)
Nombre d'heures de TV par jour	2.00 (1.00, 3.00)
Unknown	5
¹ Statistics presented: n (%); median (IQR)

Characteristic	exp(Beta)	95% CI¹	p-value
Equilibre\|Satisfaction	1.40		0.4
Niveau d'étude
Primaire	—	—
Secondaire	1.06	0.67, 1.67	0.8
Technique/Professionnel	1.03	0.68, 1.57	0.9
Supérieur	1.85	1.20, 2.84	0.005
manquant	0.63	0.24, 1.66	0.4
Insatisfaction\|Equilibre	0.13		<0.001
Sexe
Femme	—	—
Homme	0.85	0.67, 1.08	0.2
trav.imp
Le plus	—	—
Aussi	1.42	0.66, 3.03	0.4
Moins	1.03	0.49, 2.13	>0.9
Peu	0.19	0.08, 0.47	<0.001
¹ CI = Confidence Interval

Analyse	Variables	Fonction standard	Fonction `ade4`	Fonctions `FactoMineR`
ACP	plusieurs variables quantitatives	`princomp`	`dudi.pca`	`PCA`
AFC	deux variables qualitatives	`corresp`	`dudi.coa`	`CA`
ACM	plusieurs variables qualitatives	`mca`	`dudi.acm`	`MCA`
Analyse mixte	plusieurs variables quantitatives et/ou qualitatives	`FAMD`	`dudi.mix`	—

stat	cah	pam
PBC	0.703	0.712
HG	0.933	0.950
HGSD	0.930	0.946
ASW	0.528	0.568
ASWw	0.530	0.569
CH	938.972	1023.120
R2	0.682	0.700
CHsq	3351.212	3980.601
R2sq	0.884	0.901
HC	0.028	0.021

label	levels	Rapides	Lents	Inaboutis	Hors soins	Total	p
Sexe	homme	104 (26.4)	61 (29.0)	42 (25.1)	243 (44.5)	450 (34.2)	<0.001
	femme	290 (73.6)	149 (71.0)	125 (74.9)	303 (55.5)	867 (65.8)
Âge	16-29	96 (24.4)	71 (33.8)	62 (37.1)	235 (43.0)	464 (35.2)	<0.001
	30-59	269 (68.3)	128 (61.0)	91 (54.5)	281 (51.5)	769 (58.4)
	60+	29 (7.4)	11 (5.2)	14 (8.4)	30 (5.5)	84 (6.4)
Education	primaire	63 (16.0)	47 (22.4)	30 (18.0)	123 (22.5)	263 (20.0)	0.002
	secondaire	126 (32.0)	83 (39.5)	61 (36.5)	212 (38.8)	482 (36.6)
	supérieur	205 (52.0)	80 (38.1)	76 (45.5)	211 (38.6)	572 (43.4)

Facteur	OR	p-value	IC 95% bas	IC 95% haut
Inter:diagnostiqué, mais pas suivi	0,06	<0,001	0,0	0,1
Inter:suivi, mais pas sous traitement	0,03	<0,001	0,0	0,0
Inter:sous traitement, mais infection non contrôlée	0,02	<0,001	0,0	0,0
sexFfemme	2,10	<0,001	1,8	2,5
ageF30-59	1,75	<0,001	1,5	2,1
ageF60+	2,04	<0,001	1,4	3,0
educationFsecondaire	1,17	0,109	1,0	1,4
educationFsupérieur	1,40	0,002	1,1	1,7
monthF2	1,06	0,147	1,0	1,1
monthF3	1,36	<0,001	1,2	1,5
monthF4	2,79	<0,001	2,5	3,1
monthF5	3,82	<0,001	3,4	4,3
monthF6	4,69	<0,001	4,1	5,4
monthF7	5,63	<0,001	4,9	6,4
monthF8	6,40	<0,001	5,6	7,3
monthF9	7,02	<0,001	6,1	8,1
monthF10	7,49	<0,001	6,5	8,6
monthF11	7,96	<0,001	6,9	9,2
monthF12	8,42	<0,001	7,3	9,8
monthF13	9,31	<0,001	8,0	10,8
monthF14	10,11	<0,001	8,6	11,8
monthF15	10,74	<0,001	9,2	12,6
monthF16	11,46	<0,001	9,8	13,4
monthF17	11,43	<0,001	9,7	13,4
monthF18	11,53	<0,001	9,8	13,6
monthF19	11,50	<0,001	9,7	13,6
monthF20	11,70	<0,001	9,7	14,1
monthF21	11,67	<0,001	9,6	14,2
monthF22	12,60	<0,001	10,3	15,4
monthF23	12,78	<0,001	10,4	15,7
monthF24	12,76	<0,001	10,4	15,7
monthF25	13,11	<0,001	10,6	16,2
monthF26	13,60	<0,001	11,0	16,9
monthF27	14,40	<0,001	11,6	17,9
monthF28	15,65	<0,001	12,5	19,6
monthF29	14,89	<0,001	11,9	18,7
monthF30	14,98	<0,001	11,9	18,9
monthF31	14,02	<0,001	11,0	17,9
monthF32	14,27	<0,001	11,0	18,5
monthF33	14,41	<0,001	11,0	18,9
monthF34	14,73	<0,001	11,0	19,7
monthF35	16,17	<0,001	11,8	22,1
monthF36	16,07	<0,001	11,4	22,6

analyse-R

Introduction à l’analyse d’enquêtes avec R et RStudio

Contributeurs

Présentation

Table des matières

Manipuler

Analyser

Approfondir

Index

Licence

Contribuer

Présentation et Philosophie

Présentation de R

Philosophie de R

Présentation de RStudio

Installation de R et RStudio

Installation de R

Installation de RStudio

Mise à jour de R sous Windows

Voir aussi

Premier contact

L’invite de commandes

Des objets

Objets simples

Vecteurs

Des fonctions

Arguments

Quelques fonctions utiles

Aide sur une fonction

Interprétation des arguments

Autocomplétion

Premier travail avec des données

Regrouper les commandes dans des scripts

Ajouter des commentaires

Tableaux de données

Inspection visuelle des données

Structure du tableau

Accéder aux variables

La fonction str

Quelques calculs simples

Nos premiers graphiques

Et ensuite ?

Extensions (installation, mise à jour)

Présentation

Le tidyverse

Installation depuis CRAN

Installation depuis GitHub

Mise à jour des extensions

Introduction au tidyverse

Extensions

Installation

tidy data

tibbles

Vecteurs, indexation et assignation

Présentation des vecteurs

Les principaux types de vecteurs

Création

La fonction c

La fonction rep

La fonction seq

L’opérateur :

Longueur d’un vecteur

Quelques vecteurs remarquables

Combiner des vecteurs

Valeurs manquantes

Indexation par position

Des vecteurs nommés

Indexation par nom

Indexation par condition

Assignation par indexation

En résumé

Listes et Tableaux de données

Listes

Propriétés et création

Indexation

Tableaux de données

Propriétés et création

Indexation

Afficher les données

En résumé

Introduction
à l’analyse d’enquêtes
avec R et RStudio

		Format de sortie
		array	data.frame	list
Format d’entée	array	`aaply`	`adply`	`alply`
	data.frame	`daply`	`ddply`	`dlply`
	list	`laply`	`ldply`	`llply`

Fonction	Input	Output
`apply`	Matrice ou tableau	Vecteur ou tableau ou liste
`lapply`	Liste ou vecteur	Liste
`sapply`	Liste ou vecteur	Vecteur ou matrice ou liste
`vapply`	Liste ou vecteur	Vecteur ou matrice ou liste
`tapply`	Tableau et facteurs	Tableau ou liste
`mapply`	Listes et/ou vecteurs	Vecteur ou matrice ou liste

Option	Valeurs	Description
echo	TRUE/FALSE	Afficher ou non le code R dans le document
eval	TRUE/FALSE	Exécuter ou non le code R à la compilation
include	TRUE/FALSE	Inclure ou non le code R et ses résultats dans le document
results	“hide”/“asis”/“markup”/“hold”	Type de résultats renvoyés par le bloc de code
warning	TRUE/FALSE	Afficher ou non les avertissements générés par le bloc
message	TRUE/FALSE	Afficher ou non les messages générés par le bloc

	Homme	Femme	Total
Ouvrier specialise	47.29064	52.70936	100
Ouvrier qualifie	78.42466	21.57534	100
Technicien	76.74419	23.25581	100
Profession intermediaire	55.00000	45.00000	100
Cadre	55.76923	44.23077	100
Employe	16.16162	83.83838	100
Autre	36.20690	63.79310	100
Ensemble	44.82759	55.17241	100

	Homme	Femme	Total
Ouvrier specialise	47.3	52.7	100
Ouvrier qualifie	78.4	21.6	100
Technicien	76.7	23.3	100
Profession intermediaire	55.0	45.0	100
Cadre	55.8	44.2	100
Employe	16.2	83.8	100
Autre	36.2	63.8	100
Ensemble	44.8	55.2	100

	Group 1		Group 2^a
	mpg	cyl	disp	hp
Mazda RX4	21.0	6	160	110
Mazda RX4 Wag	21.0	6	160	110
Datsun 710	22.8	4	108	93
Hornet 4 Drive	21.4	6	258	110
Hornet Sportabout	18.7	8	360	175
^a table footnote

	num	char	fctr	Date et/ou Heure			currency
	num	char	fctr	date	time	datetime	currency
grp_a
row_1	0,11	apricot	one	15 janv. 2015	1:35	janv. 1, 2018 2:22	€49,95
row_2	2,22	banana	two	15 févr. 2015	2:40	févr. 2, 2018 2:33	€17,95
row_3	33,33	coconut	three	15 mars 2015	3:45	mars 3, 2018 3:44	€1,39
row_4	444,40	durian	four	15 avr. 2015	4:50	avr. 4, 2018 3:55	€65 100,00
moyenne	120.02	—	—	—	—	—	—
total	480.06	—	—	—	—	—	—
grp_b
row_5	5 550,00	NA	five	15 mai 2015	5:55	mai 5, 2018 4:00	€1 325,81
row_6	NA	fig	six	15 juin 2015	NA	juin 6, 2018 4:11	€13,26
row_7	777 000,00	grapefruit	seven	NA	7:10	juil. 7, 2018 5:22	NA
row_8	8 880 000,00	honeydew	eight	15 août 2015	8:20	NA	€0,44
moyenne	3,220,850.00	—	—	—	—	—	—
total	9,662,550.00	—	—	—	—	—	—
grand total	—	—	—	—	—	—	€66,508.79

Package and Palette Name		Color Count and Palette
awtools
a_palette	8
bpalette	16
gpalette	4
mpalette	9
spalette	6
dichromat
BrowntoBlue.10	10
BrowntoBlue.12	12
BluetoDarkOrange.12	12
BluetoDarkOrange.18	18
DarkRedtoBlue.12	12
DarkRedtoBlue.18	18
BluetoGreen.14	14
BluetoGray.8	8
BluetoOrangeRed.14	14
BluetoOrange.10	10
BluetoOrange.12	12
BluetoOrange.8	8
LightBluetoDarkBlue.10	10
LightBluetoDarkBlue.7	7
Categorical.12	12
GreentoMagenta.16	16
SteppedSequential.5	25
dutchmasters
milkmaid	13
pearl_earring	11
view_of_Delft	12
little_street	11
anatomy	7
staalmeesters	7
ggsci
nrc_npg	10
default_aaas	10
default_nejm	8
lanonc_lancet	9
default_jama	7
default_jco	10
default_ucscgb	26
category10_d3	10
category20_d3	20
category20b_d3	20
category20c_d3	20
default_igv	51
alternating_igv	2
default_locuszoom	7
default_uchicago	9
light_uchicago	9
dark_uchicago	9
hallmarks_dark_cosmic	10
hallmarks_light_cosmic	10
signature_substitutions_cosmic	6
springfield_simpsons	16
planetexpress_futurama	12
schwifty_rickandmorty	12
uniform_startrek	7
legacy_tron	7
default_gsea	12
red_material	10
pink_material	10
purple_material	10
deep-purple_material	10
indigo_material	10
blue_material	10
light-blue_material	10
cyan_material	10
teal_material	10
green_material	10
light-green_material	10
lime_material	10
yellow_material	10
amber_material	10
orange_material	10
deep-orange_material	10
brown_material	10
grey_material	10
blue-grey_material	10
ggpomological
pomological_base	7
pomological_palette	9
ggthemes
calc	12
manyeys	19
gdoc	10
fivethirtyeight	6
colorblind	8
Tableau 10	10
Tableau 20	20
Color Blind	10
Seattle Grays	5
Traffic	9
Miller Stone	11
Superfishel Stone	10
Nuriel Stone	9
Jewel Bright	9
Summer	8
Winter	10
Green-Orange-Teal	12
Red-Blue-Brown	12
Purple-Pink-Gray	12
Hue Circle	19
Classic 10	10
Classic 10 Medium	10
Classic 10 Light	10
Classic 20	20
Classic Gray 5	5
Classic Color Blind	10
Classic Traffic Light	9
Classic Purple-Gray 6	6
Classic Purple-Gray 12	12
Classic Green-Orange 6	6
Classic Green-Orange 12	12
Classic Blue-Red 6	6
Classic Blue-Red 12	12
Classic Cyclic	13
few_Light	9
few_Medium	9
few_Dark	9
excel_Atlas	6
excel_Badge	6
excel_Berlin	6
excel_Celestial	6
excel_Crop	6
excel_Depth	6
excel_Droplet	6
excel_Facet	6
excel_Feathered	6
excel_Gallery	6
excel_Headlines	6
excel_Integral	6
excel_Ion_Boardroom	6
excel_Ion	6
excel_Madison	6
excel_Main_Event	6
excel_Mesh	6
excel_Office_Theme	6
excel_Organic	6
excel_Parallax	6
excel_Parcel	6
excel_Retrospect	6
excel_Savon	6
excel_Slice	6
excel_Vapor_Trail	6
excel_View	6
excel_Wisp	6
excel_Wood_Type	6
excel_Aspect	6
excel_Blue_Green	6
excel_Blue_II	6
excel_Blue_Warm	6
excel_Blue	6
excel_Grayscale	6
excel_Green_Yellow	6
excel_Green	6
excel_Marquee	6
excel_Median	6
excel_Office_2007-2010	6
excel_Orange_Red	6
excel_Orange	6
excel_Paper	6
excel_Red_Orange	6
excel_Red_Violet	6
excel_Red	6
excel_Slipstream	6
excel_Violet_II	6
excel_Violet	6
excel_Yellow_Orange	6
excel_Yellow	6
wsj_rgby	4
wsj_red_green	2
wsj_black_green	4
wsj_dem_rep	3
wsj_colors6	6
stata_s2color	15
stata_s1rcolor	15
stata_s1color	15
stata_mono	15
stata_economist	15
hc_default	10
hc_darkunica	11
hc_bg	5
hc_fg	12
ghibli
MarnieLight1	7
MarnieMedium1	7
MarnieDark1	7
MarnieLight2	7
MarnieMedium2	7
MarnieDark2	7
PonyoLight	7
PonyoMedium	7
PonyoDark	7
LaputaLight	7
LaputaMedium	7
LaputaDark	7
MononokeLight	7
MononokeMedium	7
MononokeDark	7
SpiritedLight	7
SpiritedMedium	7
SpiritedDark	7
YesterdayLight	7
YesterdayMedium	7
YesterdayDark	7
KikiLight	7
KikiMedium	7
KikiDark	7
TotoroLight	7
TotoroMedium	7
TotoroDark	7
grDevices
blues9	9
jcolors
default	5
pal2	5
pal3	5
pal4	6
pal5	6
pal6	8
pal7	8
pal8	12
pal9	6
pal10	10
pal11	12
pal12	13
rainbow	9
LaCroixColoR
PassionFruit	6
Mango	6
Pure	6
Lime	6
Lemon	6
Orange	6
Berry	6
CranRaspberry	6
Pamplemousse	6
PeachPear	6
Coconut	6
Apricot	6
Tangerine	7
KeyLime	5
PommeBaya	5
CeriseLimon	6
PinaFraise	6
KiwiSandia	7
MelonPomelo	6
MurePepino	6
paired	14
NineteenEightyR
cobra	6
electronic_night	5
hotpink	5
malibu	5
miami1	5
miami2	5
seventies_aint_done_yet	5
sonny	5
sunset1	5
sunset2	5
sunset3	5
youngturqs	12
nord
polarnight	4
snowstorm	3
frost	4
aurora	5
lumina	5
mountain_forms	5
silver_mine	5
lake_superior	6
victory_bonds	5
halifax_harbor	6
moose_pond	8
algoma_forest	7
rocky_mountain	6
red_mountain	8
baie_mouton	7
afternoon_prarie	9
ochRe
namatjira_qual	8
namatjira_div	8
mccrea	11
parliament	8
tasmania	7
nolan_ned	5
winmar	7
olsen_qual	6
olsen_seq	14
williams_pilbara	7
healthy_reef	9
emu_woman_paired	18
galah	6
lorikeet	6
dead_reef	6
jumping_frog	5
palettetown
bulbasaur	13
ivysaur	13
venusaur	14
charmander	13
charmeleon	12
charizard	14
squirtle	14
wartortle	13
blastoise	14
caterpie	12
metapod	5
butterfree	13
weedle	11
kakuna	8
beedrill	14
pidgey	12
pidgeotto	13
pidgeot	14
rattata	13
raticate	13
spearow	15
fearow	12
ekans	12
arbok	10
pikachu	10
raichu	14
sandshrew	10
sandslash	13
nidoranf	11
nidorina	13
nidoqueen	13
nidoranm	10
nidorino	12
nidoking	11
clefairy	13
clefable	12
vulpix	13
ninetales	7
jigglypuff	11
wigglytuff	12
zubat	11
golbat	11
oddish	10
gloom	14
vileplume	13
paras	12
parasect	11
venonat	14
venomoth	11
diglett	4
dugtrio	12
meowth	13
persian	12
psyduck	10
golduck	13
mankey	14
primeape	15
growlithe	11
arcanine	14
poliwag	11
poliwhirl	9
poliwrath	9
abra	8
kadabra	14
alakazam	12
machop	13
machoke	13
machamp	13
bellsprout	13
weepinbell	14
victreebel	14
tentacool	14
tentacruel	14
geodude	6
graveler	6
golem	12
ponyta	11
rapidash	11
slowpoke	12
slowbro	13
magnemite	14
magneton	14
farfetch'd	14
doduo	9
dodrio	15
seel	11
dewgong	7
grimer	9
muk	8
shellder	9
cloyster	9
gastly	8
haunter	10
gengar	10
onix	6
drowzee	9
hypno	9
krabby	10
kingler	10
voltorb	9
electrode	10
exeggcute	9
exeggutor	14
cubone	13
marowak	14
hitmonlee	12
hitmonchan	13
lickitung	9
koffing	12
weezing	13
rhyhorn	8
rhydon	11
chansey	9
tangela	9
kangaskhan	14
horsea	10
seadra	10
goldeen	11
seaking	12
staryu	12
starmie	12
mr. mime	12
scyther	12
jynx	15
electabuzz	10
magmar	12
pinsir	9
tauros	13
magikarp	13
gyarados	15
lapras	15
ditto	6
eevee	9
vaporeon	15
jolteon	11
flareon	10
porygon	11
omanyte	12
omastar	12
kabuto	11
kabutops	9
aerodactyl	7
snorlax	14
articuno	12
zapdos	10
moltres	14
dratini	9
dragonair	12
dragonite	13
mewtwo	9
mew	6
chikorita	11
bayleef	14
meganium	14
cyndaquil	13
quilava	14
typhlosion	14
totodile	11
croconaw	13
feraligatr	14
sentret	12
furret	11
hoothoot	13
noctowl	13
ledyba	12
ledian	14
spinarak	14
ariados	14
crobat	9
chinchou	13
lanturn	14
pichu	11
cleffa	11
igglybuff	9
togepi	12
togetic	11
natu	14
xatu	15
mareep	14
flaaffy	13
ampharos	12
bellossom	13
marill	10
azumarill	10
sudowoodo	11
politoed	14
hoppip	10
skiploom	11
jumpluff	13
aipom	12
sunkern	13
sunflora	12
yanma	11
wooper	9
quagsire	9
espeon	9
umbreon	10
murkrow	12
slowking	13
misdreavus	13
unown	5
wobbuffet	8
girafarig	13
pineco	5
forretress	10
dunsparce	13
gligar	12
steelix	7
snubbull	12
granbull	11
qwilfish	12
scizor	11
shuckle	12
heracross	11
sneasel	14
teddiursa	12
ursaring	13
slugma	9
magcargo	12
swinub	9
piloswine	12
corsola	9
remoraid	9
octillery	12
delibird	13
mantine	13
skarmory	12
houndour	11
houndoom	12
kingdra	12
phanpy	9
donphan	13
porygon2	12
stantler	12
smeargle	12
tyrogue	12
hitmontop	12
smoochum	12
elekid	9
magby	10
miltank	15
blissey	9
raikou	13
entei	14
suicune	14
larvitar	9
pupitar	9
tyranitar	11
lugia	9
ho-oh	13
celebi	11
treecko	14
grovyle	14
sceptile	13
torchic	12
combusken	14
blaziken	14
mudkip	14
marshtomp	14
swampert	14
poochyena	14
mightyena	11
zigzagoon	14
linoone	11
wurmple	13
silcoon	9
beautifly	13
cascoon	8
dustox	14
lotad	13
lombre	12
ludicolo	15
seedot	11
nuzleaf	14
shiftry	13
taillow	15
swellow	15
wingull	14
pelipper	15
ralts	11
kirlia	13
gardevoir	12
surskit	12
masquerain	14
shroomish	11
breloom	15
slakoth	14
vigoroth	15
slaking	14
nincada	15
ninjask	13
shedinja	10
whismur	12
loudred	14
exploud	15
makuhita	12
hariyama	15
azurill	11
nosepass	11
skitty	11
delcatty	15
sableye	13
mawile	14
aron	9
lairon	12
aggron	12
meditite	13
medicham	13
electrike	10
manectric	13
plusle	10
minun	13
volbeat	15
illumise	15
roselia	15
gulpin	11
swalot	14
carvanha	13
sharpedo	14
wailmer	15
wailord	13
numel	15
camerupt	13
torkoal	12
spoink	13
grumpig	15
spinda	10
trapinch	10
vibrava	14
flygon	15
cacnea	14
cacturne	12
swablu	12
altaria	12
zangoose	15
seviper	14
lunatone	14
solrock	14
barboach	12
whiscash	15
corphish	13
crawdaunt	15
baltoy	7
claydol	12
lileep	12
cradily	12
anorith	14
armaldo	14
feebas	13
milotic	14
castform	9
kecleon	15
shuppet	12
banette	12
duskull	13
dusclops	11
tropius	15
chimecho	13
absol	11
wynaut	11
snorunt	15
glalie	13
spheal	12
sealeo	15
walrein	12
clamperl	14
huntail	14
gorebyss	12
relicanth	13
luvdisc	9
bagon	11
shelgon	12
salamence	13
beldum	10
metang	13
metagross	13
regirock	13
regice	10
registeel	13
latias	14
latios	14
kyogre	14
groudon	14
rayquaza	12
jirachi	12
deoxys	14
teamrocket	6
starterspairs	6
startersDark	6
pals
alphabet	26
alphabet2	26
glasbey	32
kelly	22
polychrome	36
stepped	24
tol	12
watlington	16
Polychrome
alphabet	26
dark	24
glasbey	32
green.armytage	26
kelly	22
light	24
palette36	36
rcartocolor
ag_Sunset	7
ag_GrnYl	7
Tropic	7
Temps	7
TealRose	7
Geyser	7
Fall	7
Earth	7
ArmyRose	7
Vivid	12
Safe	12
Prism	12
Pastel	12
Bold	12
Antique	12
TealGrn	7
Teal	7
SunsetDark	7
Sunset	7
RedOr	7
PurpOr	7
Purp	7
PinkYl	7
Peach	7
OrYel	7
Mint	7
Magenta	7
Emrld	7
DarkMint	7
BurgYl	7
Burg	7
BrwnYl	7
BluYl	7
BluGrn	7
RColorBrewer
BrBG	11
PiYG	11
PRGn	11
PuOr	11
RdBu	11
RdGy	11
RdYlBu	11
RdYlGn	11
Spectral	11
Accent	8
Dark2	8
Paired	12
Pastel1	9
Pastel2	8
Set1	9
Set2	8
Set3	12
Blues	9
BuGn	9
BuPu	9
GnBu	9
Greens	9
Greys	9
Oranges	9
OrRd	9
PuBu	9
PuBuGn	9
PuRd	9
Purples	9
RdPu	9
Reds	9
YlGn	9
YlGnBu	9
YlOrBr	9
YlOrRd	9
Redmonder
dPBIYlBu	11
dPBIYlPu	11
dPBIPuGn	11
dPBIPuOr	11
dPBIRdBu	11
dPBIRdGy	11
dPBIRdGn	11
qMSOStd	10
qMSO12	8
qMSO15	8
qMSOBuWarm	8
qMSOBu	8
qMSOBu2	8
qMSOBuGn	8
qMSOGn	8
qMSOGnYl	8
qMSOYl	8
qMSOYlOr	8
qMSOOr	8
qMSOOrRd	8
qMSORdOr	8
qMSORd	8
qMSORdPu	8
qMSOPu	8
qMSOPu2	8
qMSOMed	8
qMSOPap	8
qMSOMrq	8
qMSOSlp	8
qMSOAsp	8
qPBI	8
sPBIGn	9
sPBIGy1	9
sPBIRd	9
sPBIYl	9
sPBIGy2	9
sPBIBu	9
sPBIOr	9
sPBIPu	9
sPBIYlGn	9
sPBIRdPu	9
tidyquant
tq_light	12
tq_dark	12
tq_green	12
wesanderson
BottleRocket1	7
BottleRocket2	5
Rushmore1	5
Rushmore	5
Royal1	4
Royal2	5
Zissou1	5
Darjeeling1	5
Darjeeling2	5
Chevalier1	4
FantasticFox1	5
Moonrise1	4
Moonrise2	4
Moonrise3	5
Cavalcanti1	5
GrandBudapest1	4
GrandBudapest2	4
IsleofDogs1	6
IsleofDogs2	5
yarrr
basel	10
pony	9
xmen	8
decision	6
southpark	6
google	4
eternal	7
evildead	6
usualsuspects	7
ohbrother	7
appletv	6
brave	5
bugs	5
cars	5
nemo	5
rat	5
up	5
espresso	5
ipod	7
info	9
info2	14
The paletteer package is maintained by Emil Hvitfeldt. More information can be found on the paletteer site and on the CRAN info page.